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Gharbi, Mohamed, and Mohamed Aymen Labiod. "Une application comparative pour l’enseignement des méthodes de compression d’images par transformées orthogonales." J3eA 21 (2022): 2039. http://dx.doi.org/10.1051/j3ea/20222039.
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Nous proposons dans cette contribution une interface graphique comme outil pédagogique pour l’enseignement des méthodes de compression par transformées orthogonales utilisées dans l’encodage d’images. Cet outil est utilisé dans l’enseignement proposé aux étudiants en MASTER du département Développement, Recherche, Enseignement en Audiovisuel et Multimédia (DREAM) de l’Université Polytechnique Hauts-de-France (Valenciennes – France). Les offres de formation s’appuient notamment sur un enseignement du traitement de signal, des images ainsi que la compression des images et de la vidéo. Les deux aspects analogique et numérique du traitement du signal sont enseignés. Cet interface graphique a été développé pour permettre aux étudiants de mieux appréhender l’influence des différents paramètres intervenant dans une chaîne de compression numérique des images. A travers cet outil, nous proposons aux étudiants une nouvelle approche à la compréhension des transformées matricielles utilisées dans l’encodage des images et de la quantification sur la qualité des images compressées par plusieurs transformées. En effet, cet outil, par le biais de l’ajustement des différents paramètres intervenant dans le chaîne de compression, permet aux étudiants d’étudier et de comparer plusieurs transformées orthogonales à savoir la Transformée en Cosinus Discrète (DCT), la Transformée de Karhunen-Loève (KLT) sous les deux versions séparable ou non, la Transformation de Fourier Discrète (TFD) ainsi que la Transformée d’Hadamard. Ainsi, visuellement par le biais de l’image reconstruite et par le rapport signal à bruit cet outil permet aux étudiants d’apprécier le choix des paramètres qu’ils ont fixés pour l’obtention de leurs résultats.
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Smigiel, E., A. Knoll, N. Broll, and A. Cornet. "Modèles direct et inverse de la réflectométrie non spéculaire par transformée en cosinus discrète." Le Journal de Physique IV 10, PR10 (September 2000): Pr10–237—Pr10–243. http://dx.doi.org/10.1051/jp4:20001026.
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Jang, Ju-wook, Chang-hyeon Lim, Ronald Scrofano, and Viktor K. Prasanna. "Energy-Efficient Discrete Cosine Transform on FPGAs." KIPS Transactions:PartA 12A, no. 4 (August 1, 2005): 313–20. http://dx.doi.org/10.3745/kipsta.2005.12a.4.313.
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Hayati, Raisah, and Rahmadi Kurnia. "Simulasi Unjuk Kerja Discrete Wavelet Transform (DWT) dan Discrete Cosine Transform (DCT) untuk Pengolahan Sinyal Radar di Daerah yang Ber-Noise Tinggi." Jurnal Nasional Teknik Elektro 3, no. 1 (March 1, 2014): 32–43. http://dx.doi.org/10.20449/jnte.v3i1.53.
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Park, Jaehun, and Kwangsu Kim. "Image Perturbation-Based Deep Learning for Face Recognition Utilizing Discrete Cosine Transform." Electronics 11, no. 1 (December 22, 2021): 25. http://dx.doi.org/10.3390/electronics11010025.
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Face recognition, including emotion classification and face attribute classification, has seen tremendous progress during the last decade owing to the use of deep learning. Large-scale data collected from numerous users have been the driving force in this growth. However, face images containing the identities of the owner can potentially cause severe privacy leakage if linked to other sensitive biometric information. The novel discrete cosine transform (DCT) coefficient cutting method (DCC) proposed in this study combines DCT and pixelization to protect the privacy of the image. However, privacy is subjective, and it is not guaranteed that the transformed image will preserve privacy. To overcome this, a user study was conducted on whether DCC really preserves privacy. To this end, convolutional neural networks were trained for face recognition and face attribute classification tasks. Our survey and experiments demonstrate that a face recognition deep learning model can be trained with images that most people think preserve privacy at a manageable cost in classification accuracy.
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A. AlZain, Mohammed. "Efficient Scrambling-Substitution Image Security Scheme using Chaotic Arnold-Logistic Maps in the Discrete Cosine Transform." International journal of Computer Networks & Communications 14, no. 6 (November 30, 2022): 67–79. http://dx.doi.org/10.5121/ijcnc.2022.14605.
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This paper introduces an efficient scrambling-substitution image security scheme using chaotic Arnold and Logistic (Arnold-Logistic) maps in the discrete cosine transform (DCT). The Arnold map is employed as a scrambling stage while the Logistic map is employed as a substitution stage. The hybrid Arnold-Logistic mapping is performed in the DCT. The encipherment phase of the introduced DCT-based Arnold-Logistic security scheme begins by applying the DCT to the plainimage and the resulted DCT coefficient of the plainimage are scrambled for m iterations using the Arnold transformation. Then, the Arnold-based transformed DCT coefficients are substituted for n iterations using the Logistic map and the inverse of DCT (IDCT) is employed to produce the cipherimage. The decipherment phase of the introduced DCTbased Arnold-Logistic security scheme is the inverse of the encryption stage and begins by applying the DCT to the cipherimage. The resulted DCT coefficient of the cipherimage is inversely substituted for n iterations using the inverse Logistic map. Then, the inverse Logistic-based transformed DCT coefficients are inversely scrambled for m iterations using the inverse Arnold map and the IDCT is employed to produce the decrypted image. A series of test experiments are applied to investigate the introduced DCTbased Arnold-Logistic security scheme. The outcome results demonstrated the superiority of the introduced DCT-based Arnold-Logistic security scheme from the security point of view.
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Ito, Izumi. "A New Pseudo-Spectral Method Using the Discrete Cosine Transform." Journal of Imaging 6, no. 4 (March 28, 2020): 15. http://dx.doi.org/10.3390/jimaging6040015.
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The pseudo-spectral (PS) method on the basis of the Fourier transform is a numerical method for estimating derivatives. Generally, the discrete Fourier transform (DFT) is used when implementing the PS method. However, when the values on both sides of the sequences differ significantly, oscillatory approximations around both sides appear due to the periodicity resulting from the DFT. To address this problem, we propose a new PS method based on symmetric extension. We mathematically derive the proposed method using the discrete cosine transform (DCT) in the forward transform from the relation between DFT and DCT. DCT allows a sequence to function as a symmetrically extended sequence and estimates derivatives in the transformed domain. The superior performance of the proposed method is demonstrated through image interpolation. Potential applications of the proposed method are numerical simulations using the Fourier based PS method in many fields such as fluid dynamics, meteorology, and geophysics.
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Zadeh, Pooneh Bagheri, Akbar Sheikh Akbari, and Tom Buggy. "DCT image codec using variance of sub-regions." Open Computer Science 5, no. 1 (August 11, 2015): 13–21. http://dx.doi.org/10.1515/comp-2015-0003.
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AbstractThis paper presents a novel variance of subregions and discrete cosine transform based image-coding scheme. The proposed encoder divides the input image into a number of non-overlapping blocks. The coefficients in each block are then transformed into their spatial frequencies using a discrete cosine transform. Coefficients with the same spatial frequency index at different blocks are put together generating a number of matrices, where each matrix contains coefficients of a particular spatial frequency index. The matrix containing DC coefficients is losslessly coded to preserve its visually important information. Matrices containing high frequency coefficients are coded using a variance of sub-regions based encoding algorithm proposed in this paper. Perceptual weights are used to regulate the threshold value required in the coding process of the high frequency matrices. An extension of the system to the progressive image transmission is also developed. The proposed coding scheme, JPEG and JPEG2000were applied to a number of test images. Results show that the proposed coding scheme outperforms JPEG and JPEG2000 subjectively and objectively at low compression ratios. Results also indicate that the proposed codec decoded images exhibit superior subjective quality at high compression ratios compared to that of JPEG, while offering satisfactory results to that of JPEG2000.
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Asriani, Euis, Intan Muchtadi-Alamsyah, and Ayu Purwarianti. "On Block g-Circulant Matrices with Discrete Cosine and Sine Transforms for Transformer-Based Translation Machine." Mathematics 12, no. 11 (May 29, 2024): 1697. http://dx.doi.org/10.3390/math12111697.
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Transformer has emerged as one of the modern neural networks that has been applied in numerous applications. However, transformers’ large and deep architecture makes them computationally and memory-intensive. In this paper, we propose the block g-circulant matrices to replace the dense weight matrices in the feedforward layers of the transformer and leverage the DCT-DST algorithm to multiply these matrices with the input vector. Our test using Portuguese-English datasets shows that the suggested method improves model memory efficiency compared to the dense transformer but at the cost of a slight drop in accuracy. We found that the model Dense-block 1-circulant DCT-DST of 128 dimensions achieved the highest model memory efficiency at 22.14%. We further show that the same model achieved a BLEU score of 26.47%.
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Fu, Yong Gang. "A Print-Scan Resilient Image Watermarking Scheme Based on Radon Transform." Applied Mechanics and Materials 687-691 (November 2014): 3812–17. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3812.
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In this paper, a print-scan resilient image watermarking scheme based on Radon transform and DCT(discrete cosine transform) is proposed. The watermark is preprocessed with a chaotic sequence, then the host image is transformed to frequency domain by block DCT. The preprocessed watermark is adaptively embedded into the transform domain coefficients by modulating the relationship between neighbor coefficients. The watermark detection process is accomplished without referring to the original image. Simulation results show good robustness against several attacks, such Jpeg compression, cropping, filtering and so on.
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Novamizanti, Ledya, Ida Wahidah, and Ni Wardana. "A Robust Medical Images Watermarking Using FDCuT-DCT-SVD." International Journal of Intelligent Engineering and Systems 13, no. 6 (December 31, 2020): 266–78. http://dx.doi.org/10.22266/ijies2020.1231.24.
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One way to prevent image duplication is by applying watermarking techniques. In this work, the watermarking process is applied to medical images using the Fast Discrete Curvelet Transforms (FDCuT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD) methods. The medical image of the host is transformed using FDCuT so that three subbands are obtained. High Frequency (HF) subband selected for DCT and SVD applications. Meanwhile, SVD was also applied to the watermark image. The singular value on the host image is exchanged with the singular value on the watermark. Insertion of tears by exchanging singular values does not cause the quality of medical images to decrease significantly. The experimental results prove that the proposed FDCuT-DCT-SVD algorithm produces good imperceptibility. The proposed algorithm is also resistant to various types of attacks, including JPEG compression, noise enhancement attacks, filtering attacks, and other common attacks.
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HEGDE, VEENA N., RAVISHANKAR DEEKSHIT, and P. S. SATYANARAYANA. "DCT-BASED VARIABLE STEP SIZE GRIFFITHS' LMS ALGORITHM FOR RANDOM NOISE CANCELLATION IN ECGs." Journal of Mechanics in Medicine and Biology 12, no. 05 (December 2012): 1250025. http://dx.doi.org/10.1142/s021951941250025x.
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This paper presents a new random noise cancellation technique for cancelling muscle artifact effects from ECG using ALE in the transformed domain. For this a transform domain variable step size griffith least mean square (TVGLMS) algorithm is proposed. The technique is based on the adaptation of the gradient of the error surface. The method frees both the step size and the gradient from observation noise and reduces the gradient mis-adjustment error. The sluggishness introduced due to the averaging of the gradient in the time domain is overcome by the transformed domain approach. The proposed algorithm uses a discrete cosine transform (DCT)-based signal decomposition due to its improved frequency resolution compared to a discrete Fourier transform (DFT). Furthermore, as the data used symmetrical, DCT usage results in low leakage (bias and variance). The performance of the proposed method has been tested on ECG signals combined with WGN, extracted from MIT database, and compared with several existing techniques like LMS, NLMS, and VGLMS.
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Khan, Zirak, Farrukh Arslan, Faseeha Munir, Mubashir Ali, and Shahrukh . "Extracting the most important Discrete Cosine Transform (DCT) Coefficients for Image Compression using Deep Learning." VFAST Transactions on Software Engineering 10, no. 4 (November 27, 2022): 56–63. http://dx.doi.org/10.21015/vtse.v10i4.1145.
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Image compression is all about reducing storage costs and making the transmission of huge image files feasible. This paper targets lossy image compression by estimating the most important Discrete Cosine Transform (DCT) coefficients through employing deep learning. DCT basically results in the transformation of an image to the frequency domain from the spatial domain. The first few coefficients, in the frequency domain of a transformed image, have great importance. They are the most informative, while the others are of the least importance. The abilities of Multi-Layer Perceptron (MLP) and Convolutional neural network (CNN) were exploited in order to find a reasonable estimate of important DCT coefficients. The target was to get a deep neural network (DNN) for the compression of digital images that has a reduced number of DCT coefficients that is; higher compression rate and better image quality upon reconstruction and improved generalization ability. To shorten the encoding-decoding time and to fasten the training of our deep neural networks, RELUs and Tangent Sigmoid were used. Experiments performed on a large set of grayscale images shows that only 15 out of 64 total available DCT coefficients result in more than 70% image quality and have a good compression ratio of 4:1. Moreover, the quality of images upon a subjective and objective evaluation of unseen data proves that our proposed MLP achieved better generalization as compared to CNN.
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Belilita, Sarra, Nourredine Amardjia, Tewfik Bekkouche, and Imen Nouioua. "Combining SVD-DCT Image Watermarking Scheme Based on Perona-Malik Diffusion." Elektronika ir Elektrotechnika 25, no. 4 (August 7, 2019): 68–74. http://dx.doi.org/10.5755/j01.eie.25.4.23973.
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In this paper, we propose an efficient image watermarking scheme based on combining Singular Value Decomposition (SVD) and Discrete Cosine Transform (DCT) transforms. The main idea consists of exploiting results obtained by the well-known Perona-Malik diffusion applied in image de-noising in watermarking domain by selecting significant blocks to embed a binary logo watermark. After applying the DCT on the selected blocks, the SVD is performed on each of these transformed blocks to modify a few numbers of its U matrix elements according to some defined conditions. Computer simulation and experimental results prove the efficiency of our proposed method against image processing attacks and outperform several previous schemes in terms of imperceptibility and robustness.
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Abdul-Amir, Saied O., and Khamies K. Hasan. "DCT/DPCM Hybrid Coding for Interlaced Image Compression." Tikrit Journal of Engineering Sciences 16, no. 1 (March 31, 2009): 121–32. http://dx.doi.org/10.25130/tjes.16.1.09.
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By the nature of images, picture elements in local regions are highly correlated with one another. In such cases, image compression techniques are introduced to reduce the amount of data is needed to represent the same information, either exactly or approximately. In this work DCT/DPCM hybrid approach have been designed and implemented for interlaced images. The image signal was first transformed row-wise using discrete cosine transform (DCT) and a differential pulse code modulation (DPCM) scheme then was used column-wise to get difference signal. For still images the same 3-bit quantizer was employed which makes quantization process easier. For interlaced images 3-bit quantizer was used for the odd field and 2-bit quantizer for even field, since the difference signal of the even field was very small. A compression ratios of about 13:1 was obtained for interlaced image. Objective measurements showed a high peak to peak signal to noise ratio without noticeable impairment.
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Birvinskas, Darius, Vacius Jusas, Ignas Martisius, and Robertas Damasevicius. "Fast DCT algorithms for EEG data compression in embedded systems." Computer Science and Information Systems 12, no. 1 (2015): 49–62. http://dx.doi.org/10.2298/csis140101083b.
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Electroencephalography (EEG) is widely used in clinical diagnosis, monitoring and Brain - Computer Interface systems. Usually EEG signals are recorded with several electrodes and transmitted through a communication channel for further processing. In order to decrease communication bandwidth and transmission time in portable or low cost devices, data compression is required. In this paper we consider the use of fast Discrete Cosine Transform (DCT) algorithms for lossy EEG data compression. Using this approach, the signal is partitioned into a set of 8 samples and each set is DCT-transformed. The least-significant transform coefficients are removed before transmission and are filled with zeros before an inverse transform. We conclude that this method can be used in real-time embedded systems, where low computational complexity and high speed is required.
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Aggarwal, Vibha, and Manjeet Singh Patterh. "PERFORMANCE ANALYSIS OF NONLINEAR TRANSFORMATIONAL ECG COMPRESSION METHOD." Biomedical Engineering: Applications, Basis and Communications 21, no. 04 (August 2009): 245–51. http://dx.doi.org/10.4015/s1016237209001350.
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This paper presents and analyzes nonlinear transform-based method electrocardiogram (ECG) compression. The procedure used is similar to that used in linear transform-based method. The ECG signal is first transformed using (i) linear transform: discrete cosine transforms (DCT), Laplacian pyramid (LP), wavelet transform (WT) and it is transformed using (ii) nonlinear transform: essentially nonoscillatory cell average (ENOCA). The transformed coefficients (TC) are thresholded using the bisection algorithm in order to match the predefined user-specified percentage root mean square difference (PRD) within the tolerance. Then, the binary lookup table is made to store the position map for zero and nonzero coefficients (NZCs). The NZCs are quantized by Max–Lloyd quantizer followed by arithmetic coding. Lookup table is encoded by Huffman coding. The results are presented on different ECG signals of varying characteristics. The results show that nonlinear transform (ENOCA) gives better performance at high PRD where as at low PRD, DCT performs better.
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Bazine, Razika, Huayi Wu, and Kamel Boukhechba. "Spatial Filtering in DCT Domain-Based Frameworks for Hyperspectral Imagery Classification." Remote Sensing 11, no. 12 (June 13, 2019): 1405. http://dx.doi.org/10.3390/rs11121405.
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In this article, we propose two effective frameworks for hyperspectral imagery classification based on spatial filtering in Discrete Cosine Transform (DCT) domain. In the proposed approaches, spectral DCT is performed on the hyperspectral image to obtain a spectral profile representation, where the most significant information in the transform domain is concentrated in a few low-frequency components. The high-frequency components that generally represent noisy data are further processed using a spatial filter to extract the remaining useful information. For the spatial filtering step, both two-dimensional DCT (2D-DCT) and two-dimensional adaptive Wiener filter (2D-AWF) are explored. After performing the spatial filter, an inverse spectral DCT is applied on all transformed bands including the filtered bands to obtain the final preprocessed hyperspectral data, which is subsequently fed into a linear Support Vector Machine (SVM) classifier. Experimental results using three hyperspectral datasets show that the proposed framework Cascade Spectral DCT Spatial Wiener Filter (CDCT-WF_SVM) outperforms several state-of-the-art methods in terms of classification accuracy, the sensitivity regarding different sizes of the training samples, and computational time.
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DUAN, FABING, DEREK ABBOTT, and FRANÇOIS CHAPEAU-BLONDEAU. "EVALUATION OF THE SIGN DETECTOR FOR DCT DOMAIN WATERMARK DETECTION." Fluctuation and Noise Letters 10, no. 04 (December 2011): 337–58. http://dx.doi.org/10.1142/s0219477511000612.
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Reordering by the rule of decreased absolute amplitudes, the discrete cosine transformation (DCT) coefficients of an image are approximately modeled as dichotomous noise. Based on this assumption, it is interesting to note that the classical multiplicative embedding method can be transformed into an additive embedding rule, which accords with the signal processing problem of detecting a known weak signal in additive non-Gaussian noise. Then, following the generalized Neyman-Pearson lemma, a locally optimum detector, named the sign detector, is introduced to distinguish the correct watermark from the wrong ones. The statistical characteristics of this nonlinear sign detector are analytically investigated in detail. Extensive experimental results demonstrate the robustness of watermark against some common attacks, e.g., JPEG compression, cropping, filtering, additive Gaussian noise, dithering, and also verify the robust performance of the nonlinear sign detector for watermark detection.
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Zear, Aditi, Amit Kumar Singh, and Pardeep Kumar. "Multiple Watermarking for Healthcare Applications." Journal of Intelligent Systems 27, no. 1 (January 26, 2018): 5–18. http://dx.doi.org/10.1515/jisys-2016-0036.
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AbstractIn this paper, an algorithm for multiple digital watermarking based on discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) was proposed for healthcare applications such as tele-ophthalmology, tele-medicine, tele-diagnosis, and tele-consultancy services. Multiple watermarks were used in this algorithm to reduce the consequences of medical identity thefts. In the embedding process, the cover medical image was decomposed into third-level DWT. Low-frequency bands (LH2 and LL3) were transformed by DCT, and then SVD was applied to DCT coefficients. Two watermarks in the form of images were also transformed by DCT and then SVD. The singular values of the watermark information were embedded in the singular value of the cover medical image. Watermarks were extracted using an extraction algorithm. In order to enhance the robustness performance of the image watermarks, back-propagation neural network was applied to the extracted watermarks to reduce the effects of different noise applied on the watermarked image. Results were obtained by varying the gain factor and the different cover image modalities. Experimental results were provided to illustrate that the proposed method is able to withstand a variety of signal processing attacks, and has been found to give excellent performance in terms of robustness and imperceptibility. The performance of the method was also compared with other reported techniques. Further, the visual quality of the proposed method was also evaluated by a subjective method.
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RENDRAGRAHA, RENDY DWI, GELAR BUDIMAN, and IRMA SAFITRI. "QIM - Based Audio Watermarking with Combination Technique of DCT-QR-CPT." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 7, no. 1 (January 24, 2019): 112. http://dx.doi.org/10.26760/elkomika.v7i1.112.
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ABSTRAKAudio watermarking adalah teknik memasukkan informasi ke dalam file audio dan untuk melindungi hak cipta data digital dari distribusi ilegal. Makalah ini memperkenalkan audio stereo watermarking berdasarkan Quantization Index Modulation (QIM) dengan teknik gabungan Discrete Cosine Transform (DCT) - QRCartesian Polar Transform (CPT). Host audio dibagi menjadi beberapa frame, selanjutnya setiap frame ditransformasi oleh DCT, kemudian output DCT diuraikan menjadi matriks orthogonal dan matriks segitiga menggunakan metode QR. Selanjutnya, CPT mengubah dua koefisien kartesian dari matriks segitiga (R) pada posisi (1,1) dan (2,2) menjadi koefisien polar. Setelah itu, penyisipan dilakukan pada koefisien polar oleh QIM. Hasil simulasi menunjukkan bahwa imperseptibilitas audio terwatermark berkualitas baik dengan Signal to Noise Ratio (SNR)> 20, Mean Opinion Score (MOS)> 4 dan tahan terhadap serangan seperti Low Pass Filter (LPF) dan Band Pass Filter (BPF) dengan cut off 25-6k, resampling, Linear Speed Change (LSC) dan MP3 Compression dengan rate diatas 64 kbps.Kata kunci: Audio Watermarking, CPT, DCT, QIM, QR ABSTRACTAudio watermarking is a technique for inserting information into an audio file and to protect the copyright of digital data from illegal distribution. This paper introduces a stereo audio watermarking based on Quantization Index Modulation (QIM) with combined technique Discrete Cosine Transform (DCT) – QR – Cartesian Polar Transform (CPT). Each frame of a host audio is transformed by DCT, then DCT output is decomposed using QR method. Next, CPT transform two cartesian coefficients from triangular matrix (R) in position (1,1) and (2,2) to polar coefficients. After that, embedding is executed on polar coefficients by QIM. The simulation result shows that the imperceptibility is good with Signal to Noise Ratio (SNR)>20, Mean Opinion Score (MOS)>4 and it is robust against attacks such as Low Pass Filter (LPF) and Band Pass Filter (BPF) with cut off 25-6k, Resampling, Linear Speed Change and MP3 Compression with rate 64 kbps and above. Keywords: Audio Watermarking, CPT, DCT, QIM, QR
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Zhang, Shun, Tie-gang Gao, and Fu-sheng Yang. "A High Capacity Reversible Data Hiding Scheme Based on Multi-Level Integer DWT and Histogram Modification." International Journal of Digital Crime and Forensics 6, no. 1 (January 2014): 51–64. http://dx.doi.org/10.4018/ijdcf.2014010104.
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A reversible data hiding scheme based on integer DWT and histogram modification is proposed. In the scheme, the cover media is firstly transformed by Integer DWT (Discrete Wavelet Transformation); then information is embedded through the modification of histograms of the middle and high frequency sub-bands of the DWT coefficients. In order to increase the embedding capacity, a multi-level scheme is proposed, which achieved both high embedding capacity and reversibility. Extensive experimental results have shown that the proposed scheme achieves both higher embedding capacity and lower distortion than spatial domain histogram modification based schemes; and it achieved better performance than integer DCT (Discrete Cosine Transformation) based histogram modification scheme.
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Tsai, Tienwei, Te-Wei Chiang, and Yo-Ping Huang. "An Efficient DCT-Based Image Retrieval Approach Using Distance Threshold Pruning." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 3 (May 20, 2008): 268–76. http://dx.doi.org/10.20965/jaciii.2008.p0268.
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Content-based image retrieval (CBIR) techniques would allow indexing and retrieving images based on their low-level contents, which involves a large number of image pixels and thus becomes an inherently and essentially computational intensive task. This paper proposes a distance threshold pruning (DTP) method to alleviate computational burden of CBIR without sacrificing its accuracy. In our approach, the images are converted into the YUV color space, and then transformed into discrete cosine transform (DCT) coefficients. Benefited from the energy compacting property of DCT, Only the low-frequency DCT coefficients of Y, U, and V components are stored. On querying an image, at the first stage, the DTP serves as a filter to remove those candidates with widely distinct features. At the second stage, the detailed similarity comparison (DSC) is performed on those remaining candidates passing through the first stage. The experimental results show that both high efficacy and high data reduction rate can be achieved simultaneously by using the proposed approach.
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Roy, Soumitra, and Arup Kumar Pal. "An indirect watermark hiding in discrete cosine transform–singular value decomposition domain for copyright protection." Royal Society Open Science 4, no. 6 (June 2017): 170326. http://dx.doi.org/10.1098/rsos.170326.
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Digital image watermarking has emerged as a promising solution for copyright protection. In this paper, a discrete cosine transform (DCT) and singular value decomposition (SVD) based hybrid robust image watermarking method using Arnold scrambling is proposed and simulated to protect the copyright of natural images. In this proposed scheme, before embedding, watermark is scrambled with Arnold scrambling. Then, the greyscale cover image and encrypted watermark logo are decomposed into non-overlapping blocks and subsequently some selected image blocks are transformed into the DCT domain for inserting the watermark blocks permanently. For better imperceptibility and effectiveness, in this proposed algorithm, watermark image blocks are embedded into singular values of selected blocks by multiplying with a feasible scaling factor. Simulation result demonstrates that robustness is achieved by recovering satisfactory watermark data from the reconstructed cover image after applying common geometric transformation attacks (such as rotation, flip operation, cropping, scaling, shearing and deletion of lines or columns operation), common enhancement technique attacks (such as low-pass filtering, histogram equalization, sharpening, gamma correction, noise addition) and jpeg compression attacks.
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Mokashi, Bhargavi, Vandana S. Bhat, Jagadeesh D. Pujari, S. Roopashree, T. R. Mahesh, and D. Stalin Alex. "Efficient Hybrid Blind Watermarking in DWT-DCT-SVD with Dual Biometric Features for Images." Contrast Media & Molecular Imaging 2022 (September 8, 2022): 1–14. http://dx.doi.org/10.1155/2022/2918126.
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In the modern era of virtual computers over the notional environment of computer networks, the protection of influential documents is a major concern. To bring out this motto, digital watermarking with biometric features plays a crucial part. It utilizes advanced technology of cuffing data into digital media, i.e., text, image, video, or audio files. The strategy of cuffing an image inside another image by applying biometric features namely signature and fingerprint using watermarking techniques is the key purpose of this study. To accomplish this, a combined watermarking strategy consisting of Discrete Wavelet Transform, Discrete Cosine Transform, and Singular Value Decomposition (DWT-DCT-SVD) is projected for authentication of image that is foolproof against attacks. Here, singular values of watermark1 (fingerprint) and watermark2 (signature) are obtained by applying DWT-DCT-SVD. Affixing both the singular values of watermarks, we acquire the transformed watermark. Later, the same is applied to cover image to extract the singular values. Then we add these values to the cover image and transformed watermark to obtain a final watermarked image containing both signature and fingerprint. To upgrade the reliability, sturdiness, and originality of the image, a fusion of watermarking techniques along with dual biometric features is exhibited. The experimental results conveyed that the proposed scheme achieved an average PSNR value of about 40 dB, an average SSIM value of 0.99, and an embedded watermark resilient to various attacks in the watermarked image.
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Kim, Sungil, Baehyun Min, Kyungbook Lee, and Hoonyoung Jeong. "Integration of an Iterative Update of Sparse Geologic Dictionaries with ES-MDA for History Matching of Channelized Reservoirs." Geofluids 2018 (July 29, 2018): 1–21. http://dx.doi.org/10.1155/2018/1532868.
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This study couples an iterative sparse coding in a transformed space with an ensemble smoother with multiple data assimilation (ES-MDA) for providing a set of geologically plausible models that preserve the non-Gaussian distribution of lithofacies in a channelized reservoir. Discrete cosine transform (DCT) of sand-shale facies is followed by the repetition of K-singular value decomposition (K-SVD) in order to construct sparse geologic dictionaries that archive geologic features of the channelized reservoir such as pattern and continuity. Integration of ES-MDA, DCT, and K-SVD is conducted in a complementary way as the initially static dictionaries are updated with dynamic data in each assimilation of ES-MDA. This update of dictionaries allows the coupled algorithm to yield an ensemble well conditioned to static and dynamic data at affordable computational costs. Applications of the proposed algorithm to history matching of two channelized gas reservoirs show that the hybridization of DCT and iterative K-SVD enhances the matching performance of gas rate, water rate, bottomhole pressure, and channel properties with geological plausibility.
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D HARIKA and SYED NOORULLAH. "IMPLEMENTATION OF IMAGE AUTHENTICATION USING DIGITAL WATERMARKING WITH BIOMETRIC." international journal of engineering technology and management sciences 7, no. 1 (2023): 154–67. http://dx.doi.org/10.46647/ijetms.2023.v07i01.023.
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The rapid global development of E-commerce in terms of digitalization and distribution of digital contents in the form of image, audio, video, increases the possibility of unrestricted duplication and broadcasting of copyrighted data and the protection of crucial documents is highly significant. Digital watermarking inserts watermark into the cover or host data by unnoticeable modification. In this work digital watermarking with biometric features is done. In this work a technique to implement the hiding of an image inside another image using biometric features namely signature and fingerprint using watermarking techniques is done. To accomplish this, a hybrid watermarking scheme consisting of Discrete Wavelet Transform, Discrete Cosine Transform and Singular Value Decomposition (DWT-DCT-SVD) is proposed for image authentication that is robust against attacks. Here, singular values of watermark1 (fingerprint) and watermark2 (signature) are obtained by applying DWT-DCT-SVD. By adding both the singular values of watermarks we acquire the transformed watermark. To improve the security, robustness and provide authenticity for the image, a two-step watermarking method is demonstrated. The evaluation parameters like PSNR (Peak Signal to Noise Ratio), SSIM (Structured Similarity Index Method), normalized correlation coefficient (NCC) are used for image quality assessment.
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Iwut, Iwan, Gelar Budiman, and Ledya Novamizanti. "Optimization of Discrete Cosine Transform-Based Image Watermarking by Genetics Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 1 (October 1, 2016): 91. http://dx.doi.org/10.11591/ijeecs.v4.i1.pp91-103.
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<p align="center">data in an image file is needed by its owner to set his ownership in a logo as a watermark embedded in the image file. Hiding the logo in the image was done in several methods. One of the method is domain transform using 2D-DCT in which data is embedded in frequency domain of the image. First, the host RGB image is converted to certain color space. The available and chosen color spaces are RGB, YCbCr or NTSC. The layer in which the watermark is embedded also can be selected. The available choices are 1<sup>st</sup> layer, 2<sup>nd</sup> layer, 3<sup>rd</sup> layer, 1<sup>st</sup> & 2<sup>nd</sup> layer, 2<sup>nd</sup> & 3<sup>rd</sup> layer, 1<sup>st</sup> & 3<sup>rd</sup> layer and all layers. After the selected layer of image in certain color space is transformed in block based to frequency domain by DCT, one bit watermark is embedded on the AC coefficient of each block such a way that the bit is represented by specific value called delta in a zigzag and vary length of pixel. The vary parameters optimized by Genetics Algorithm are selected color space, selected layer, block size, length of pixel to be embedded by one bit watermark, and delta. Bit “1” is represented by +delta, and bit “0” is represented by –delta in vary length of pixel after zigzag. The simulation result performs that GA is useful to search the value of parameter that produces controllable the combination between robustness, invisibility and capacity. Thus, GA improves the method by determining the exact value of parameter achieving BER, PSNR and payload. </p>
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Moussa, Mourad, Maha Hmila, and Ali Douik. "Face recognition using fractional coefficients and discrete cosine transform tool." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (February 1, 2021): 892. http://dx.doi.org/10.11591/ijece.v11i1.pp892-899.
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Face recognition is a computer vision application based on biometric information for automatic person identification or verification from image sequence or a video frame. In this context DCT is the easy technique to determine significant parameters. Until now the main object is selection of the coefficients to obtain the best recognition. Many techniques rely on premasking windows to discard the high and low coefficients to enhance performance. However, the problem resides in the shape and size of premask. To improve discriminator ability in discrete cosine transform domain, we used fractional coefficients of the transformed images with discrete cosine transform to limit the coefficients area for a better performance system. Then from the selected bands, we use the discrimination power analysis to search for the coefficients having the highest power to discriminate different classes from each other. Feature selection algorithm is a key issue in all pattern recognition system, in fact this algorithm is utilized to define features vector among several ones, where these features are selected according a specified discrimination criterion. Many classifiers are used to evaluate our approach like, support vector machine and random forests. The proposed approach is validated with Yale and ORL Face databases. Experimental results prove the sufficiency of this method in face and facial expression recognition field.
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Jafarpour, Behnam, Vivek K. Goyal, Dennis B. McLaughlin, and William T. Freeman. "Transform-domain sparsity regularization for inverse problems in geosciences." GEOPHYSICS 74, no. 5 (September 2009): R69—R83. http://dx.doi.org/10.1190/1.3157250.
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We have developed a new regularization approach for estimating unknown spatial fields, such as facies distributions or porosity maps. The proposed approach is especially efficient for fields that have a sparse representation when transformed into a complementary function space (e.g., a Fourier space). Sparse transform representations provide an accurate characterization of the original field with a relatively small number of transformed variables. We use a discrete cosine transform (DCT) to obtain sparse representations of fields with distinct geologic features, such as channels or geologic formations in vertical cross section. Low-frequency DCT basis elements provide an effectively reduced subspace in which the sparse solution is searched. The low-dimensional subspace is not fixed, but rather adapts to the data.The DCT coefficients are estimated from spatial observations with a variant of compressed sensing. The estimation procedure minimizes an [Formula: see text]-norm measurement misfit term while maintaining DCT coefficient sparsity with an [Formula: see text]-norm regularization term. When measurements are noise-dominated, the performance of this procedure might be improved by implementing it in two steps — one that identifies the sparse subset of important transform coefficients and one that adjusts the coefficients to give a best fit to measurements. We have proved the effectiveness of this approach for facies reconstruction from both scattered- point measurements and areal observations, for crosswell traveltime tomography, and for porosity estimation in a typical multiunit oil field. Where we have tested our sparsity regulariza-tion approach, it has performed better than traditional alter-natives.
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Chang, Ko Chin. "Multi-Focus Image Fusion Using Local Energy Pattern." Applied Mechanics and Materials 145 (December 2011): 119–23. http://dx.doi.org/10.4028/www.scientific.net/amm.145.119.
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For general image capture device, it is difficult to obtain an image with every object in focus. To solve the fusion issue of multiple same view point images with different focal settings, a novel image fusion algorithm based on local energy pattern (LGP) is proposed in this paper. Firstly, each focus images is decomposed using discrete wavelet transform (DWT) separately. Secondly, to calculate LGP with the corresponding pixel and its surrounding pixels, then use LGP to compute the new coefficient of the pixel from each transformed images with our proposed weighted fusing rules. The rules use different operations in low-bands coefficients and high-bands coefficients. Finally, the generated image is reconstructed from the new subband coefficients. Moreover, the reconstructed image can represent more detailed for the obtained scene. Experimental results demonstrate that our scheme performs better than the traditional discrete cosine transform (DCT) and discrete wavelet transform (DWT) method in both visual perception and quantitative analysis.
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Nevart A. Minas and Faten H. Al-Qadhee. "Digital Video Compression Using DCT-Based Iterated Function System (IFS)." Tikrit Journal of Pure Science 22, no. 6 (January 30, 2023): 125–30. http://dx.doi.org/10.25130/tjps.v22i6.800.
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Large video files processing involves a huge volume of data. The codec, storage systems and network needs resource utilization, so it becomes important to minimize the used memory space and time to distribute these videos over the Internet using compression techniques. Fractal image and video compression falls under the category of lossy compression. It gives best results when used for natural images.
This paper presents an efficient method to compress an AVI (Audio Video Interleaved) file with fractal video compression(FVC). The video first is separated into a sequence of frames that are a color bitmap images, then images are transformed from RGB color space to Luminance/Chrominance components (YIQ) color space; each of these components is compressed alone with Enhanced Partition Iterated Function System (EPIFS), then fractal codes are saved.
The classical IFS suffers from a very long encoding time that needed to find the best matching for each range block when compared with the domain image blocks. In this work, the (FVC) is enhanced by enhancing the IFS of the fractal image compression using a classification scheme based on the Discrete Cosine Transform (DCT). Experimentation is performed by considering different block sizes and jump steps to reduce number of the tested domain blocks. Results shows a significant reduction in the encoding time with good quality and high compression ratio for different video files.
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Hu, Xin Ying, and Xiu Ping Zhao. "A Robust Digital Watermarking Algorithm Based on Digital Image Security." Advanced Materials Research 174 (December 2010): 144–47. http://dx.doi.org/10.4028/www.scientific.net/amr.174.144.
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Digital watermarking has been proposed as a way to claim the ownership of the source and owner of the digital image data. In this paper, A robust algorithm based on DCT region is proposed to improve the image security. The main transforming is based on DCT (Discrete Cosine Transform). The algorithm was processed in the MATLAB software. In this paper, images of gray scale and RGB color scale were researched respectively. For color images, in order to get the best image quality, the RGB scale was transformed to YcbCr scale. Then, the Y channel (brightness channel) was separated, in which the watermark was embedded and extracted. The results show that the algorithm embedded a certain size of black and white Bitmap image into gray and color images. The watermark can’t be seen by the naked eye. The robustness detection experiment was also carried out. The watermark can still be extracted after certain amount of tailoring, defacing, Gaussian noise, and format changes. The similarity is more than 0.7. It confirmed that the algorithm is highly robust.
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Khalifa, Isamadeen A., Subhi R. M. Zeebaree, Musa Ataş, and Farhad M. Khalifa. "Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and Bpnn." Science Journal of University of Zakho 7, no. 1 (March 30, 2019): 27–32. http://dx.doi.org/10.25271/sjuoz.2019.7.1.574.
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In the last two decades, steganalysis has become a fertile research area to minimize the security risks left behind by Misuse of data concealment in digital computer files. As the propagation of hidden writing increased, the need for the steganalysis emerged and grew to a large extent necessary to deter illicit secret communications. This paper introduces a steganalysis system to detect hidden information in images through using co-occurrence matrix, frequency domain transform, the first three moments, and back propagation neural network (BPNN). Four varieties of the system implemented. Firstly, the co-occurrence matrix calculated for the input image, which suspected to be a carrier of hidden secret information. Second, three levels of discrete wavelet transform (DWT) are applied resulting in 12 subbands. Then, those subbands along with the original image are transformed by discrete Fourier transform (DFT) or discrete cosine transform (DCT) to produce 13 subbands. After that, the first three moments are calculated resulting feature vector with 39 features. Finally, BPNN is used as a classifier to determine whether the image is containing hidden information or not. The system is tested with and without co-occurrence matrix, each of them once using DFT and another time using DCT. The results have shown that using co-occurrence matrix with DFT has the highest performance, which was 81.82% on the Hiding Ratio of 0.5 bit per pixel. This work demonstrates a good effect comparing to previous works.
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Mohammed Mustafa Siddeq. "Image Compression using Fourier Transformation with Genetic Algorithm." Journal of Wasit for Science and Medicine 4, no. 1 (November 8, 2022): 1–11. http://dx.doi.org/10.31185/jwsm.129.
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This paper introduce proposed algorithm consist of: (1) using Discreet Fourier Transformation (DFT) which convert an image into frequency domain image, then compress frequency domain image with Run-Length-Encoding (RLE), and Arithmetic Coding. (2) Apply Inverse Fourier Transformation (IDFT) to obtains an approximately original image, and then compared with original image to get the difference stored in a new matrix called (D(spatial) ). The matrix D(spatial) transformed to frequency domain by Discreet Cosine Transform (DCT) (D(frequency)). Finally applying weight vector (W= [0.5, 0.3, 0.2]) on the D(frequency), multiply "W" with each three coefficients from matrix D(frequency) to produce a new matrix (G), at last compress matrix G by arithmetic coding. (3) The decompression process start from arithmetic decoding to return frequency domain matrix (i.e. return DFT image), then apply Inverse DFT to get an image A, also from arithmetic decoding produced matrix G. The Genetic Algorithm used to produce minimized matrix D(frequency) by take each data from matrix G and using fitness function. Finally apply inverse DCT to generate matrix D(spatial), added with image A to produce a decompressed image. In this paper our approach, compared with JPEG technique, by using Peak Signal to Noise Ratio (PSNR).
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Rafea, S., and Dr N. H. Salman. "Hybrid DWT-DCT compression algorithm & a new flipping block with an adaptive RLE method for high medical image compression ratio." International Journal of Engineering & Technology 7, no. 4 (August 14, 2018): 4602. http://dx.doi.org/10.14419/ijet.v7i4.25904.
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Huge number of medical images are generated and needs for more storage capacity and bandwidth for transferring over the networks. Hybrid DWT-DCT compression algorithm is applied to compress the medical images by exploiting the features of both techniques. Discrete Wavelet Transform (DWT) coding is applied to image YCbCr color model which decompose image bands into four subbands (LL, HL, LH and HH). The LL subband is transformed into low and high frequency components using Discrete Cosine Transform (DCT) to be quantize by scalar quantization that was applied on all image bands, the quantization parameters where reduced by half for the luminance band while it is the same for the chrominance bands to preserve the image quality, the zigzag scan is applied on the quantized coefficients and the output are encoded using DPCM, shift optimizer and shift coding for DC while adaptive RLE, shift optimizer then shift coding applied for AC, the other subbands; LH, HL and HH are compressed using the scalar quantization, Quadtree and shift optimizer then shift coding. In this paper, a new flipping block with an adaptive RLE is proposed and applied for image enhancement. After applying DCT system and scalar quantization, huge number of zeros produced with less number of other values, so an adaptive RLE is used to encode this RUN of zeros which results with more compression.Standard medical images are selected to be used as testing image materials such as CT-Scan, X-Ray, MRI these images are specially used for researches as a testing samples. The results showed high compression ratio with high quality reconstructed images
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Zhao, Yang, Yigang Wang, Hoi-Kyung Jung, Yongqiang Jin, Dan Hua, and Sen Xu. "Lightweight Smoke Recognition Based on Deep Convolution and Self-Attention." Mathematical Problems in Engineering 2022 (August 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/1218713.
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Deep convolutional networks have better smoke recognition performance. However, a lightweight network model and high recognition accuracy cannot be balanced when deployed on hardware with limited computing resources such as edge computing. Based on this background, we propose a novel smoke recognition network that combines convolutional networks (CNN) and self-attention. The core ideas of this framework are as follows: (1) Combine the depthwise convolution and asymmetric convolution of large convolution kernels to construct a lightweight CNN model, and realize multiscale extraction of feature information with slight model complexity. (2) Combined with the self-attention in transformer, a skip-connection branch is designed, which improves the feature extraction capability of the backbone network through parallel processing and fusion of feature map information. (3) Fusion multicomponent discrete cosine transform (DCT) is used to compress channel information and expand the ability of global average pooling (GAP) to aggregate feature maps. The proposed DCT-GAP improves the accuracy of the network without adding additional computational costs. Experimental results show that the proposed CSANet achieves an average accuracy of over 98.3% with 238 M FLOPs and 5.8 M parameters on the homemade smoke dataset, outperforming state-of-the-art competitors.
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Jittawiriyanukoon, Chanintorn, and Vilasinee Srisarkun. "Evaluation of graphic effects embedded image compression." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 6606. http://dx.doi.org/10.11591/ijece.v10i6.pp6606-6617.
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A fundamental factor of digital image compression is the conversion processes. The intention of this process is to understand the shape of an image and to modify the digital image to a grayscale configuration where the encoding of the compression technique is operational. This article focuses on an investigation of compression algorithms for images with artistic effects. A key component in image compression is how to effectively preserve the original quality of images. Image compression is to condense by lessening the redundant data of images in order that they are transformed cost-effectively. The common techniques include discrete cosine transform (DCT), fast Fourier transform (FFT), and shifted FFT (SFFT). Experimental results point out compression ratio between original RGB images and grayscale images, as well as comparison. The superior algorithm improving a shape comprehension for images with grahic effect is SFFT technique.
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El Banhawy, Magy, Walaa Saber, and Fathy Amer. "A native enhanced elastic extension tables multi-tenant database." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 6618. http://dx.doi.org/10.11591/ijece.v10i6.pp6618-6628.
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A fundamental factor of digital image compression is the conversion processes. The intention of this process is to understand the shape of an image and to modify the digital image to a grayscale configuration where the encoding of the compression technique is operational. This article focuses on an investigation of compression algorithms for images with artistic effects. A key component in image compression is how to effectively preserve the original quality of images. Image compression is to condense by lessening the redundant data of images in order that they are transformed cost-effectively. The common techniques include discrete cosine transform (DCT), fast Fourier transform (FFT), and shifted FFT (SFFT). Experimental results point out compression ratio between original RGB images and grayscale images, as well as comparison. The superior algorithm improving a shape comprehension for images with grahic effect is SFFT technique.
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Shaheen, Imad, Abdelhalim Zekry, Fatma Newagy, and Reem Ibrahim. "PAPR reduction for FBMC/OQAM using hybrid scheme of different Precoding transform and mu-law companding." International Journal of Engineering & Technology 6, no. 4 (November 14, 2017): 154. http://dx.doi.org/10.14419/ijet.v6i4.8326.
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The filter banks multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) is developing multicarrier modulation technique used in the next wireless communication system (5G). FBMC/OQAM supports high data rate and high band width efficiency. However, one of the major drawbacks of FBMC system is high peak to Average Power Ratio (PAPR) of the transmitted signal, which causes serious degradation in performance of the system. Therefore, it is required to use a proper PAPR scheme at the transmitter to reduce the PAPR. In this paper, a hybrid scheme is investigated with the combination of preceding transform technique and Mu Law Companding technique to reduce PAPR in FBMC systems. Moreover, four preceding techniques are examined to find the best Precoding technique which can be used with Mu law commanding. We assessed the discrete Hartley transform (DHT). The discrete cosine transformed (DCT), the Discrete Sine Transform (DST), and the Walsh Hadamard transforms (WHT) which are applied separately with Mu Companding. The numerical results verify that the FBMC systems with all Precoding technique combined with Mu law commanding can improve PAPR performance of the signals greatly with the best results achieved when the combination scheme consists of the DST Precoding and Mu law commanding for both PAPR and BER performance.
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Saif, Waddah S., Amr M. Ragheb, Bernd Nebendahl, Tariq Alshawi, Mohamed Marey, and Saleh A. Alshebeili. "Machine Learning-Based Optical Performance Monitoring for Super-Channel Optical Networks." Photonics 9, no. 5 (April 28, 2022): 299. http://dx.doi.org/10.3390/photonics9050299.
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In this paper, and for the first time in literature, optical performance monitoring (OPM) of super-channel optical networks is considered. In particular, we propose a novel machine learning OPM technique based on the use of transformed in-phase quadrature histogram (IQH) features and support vector regressor (SVR) to estimate different optical parameters such as optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD). Two transformation methods, the two-dimensional (2D) discrete Fourier transform (DFT) and 2D discrete cosine transform (DCT), are applied to the IQH to extract features with a considerably reduced dimensionality. For the purpose of simulation, the OPM of a 7 × 20 Gbaud dual-polarization–quadrature phase shift keying (DP-QPSK) is considered. Simulations reveal that it can accurately estimate the various optical parameters (i.e., OSNR and CD) with a coefficient of determination value greater than 0.98. In addition, the effectiveness of proposed OPM scheme is examined under different values of polarization mode dispersion and frequency offset, as well as the utilization of different higher order modulation formats. Moreover, proof-of-concept experiments are performed for validation. The results show an excellent matching between the simulation and experimental findings.
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Zhang, Jun, Xiaoyi Zhou, Jilin Yang, Chunjie Cao, and Jixin Ma. "Adaptive Robust Blind Watermarking Scheme Improved by Entropy-Based SVM and Optimized Quantum Genetic Algorithm." Mathematical Problems in Engineering 2019 (October 28, 2019): 1–16. http://dx.doi.org/10.1155/2019/7817809.
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With the intensive study of machine learning in digital watermarking, its ability to balance the robustness and transparency of watermarking technology has attracted researchers’ attention. Therefore, quantum genetic algorithm, which serves as an intelligent optimized scheme combined with biological genetic mechanism and quantum computing, is widely used in various fields. In this study, an adaptive robust blind watermarking algorithm by means of optimized quantum genetics (OQGA) and entropy classification-based SVM (support vector machine) is proposed. The host image was divided into two parts according to the odd and even rows of the host image. One part was transformed by DCT (discrete cosine transform), and then the embedding intensity and position were separately trained by entropy-based SVM and OQGA; the other part was by DWT (discrete wavelet transform), in which the key fusion was achieved by an ergodic matrix to embed the watermark. Simulation results indicate the proposed algorithm ensures the watermark scheme transparency as well as having better resistance to common attacks such as lossy JPEG compression, image darken, Gaussian low-pass filtering, contrast decreasing, salt-pepper noise, and geometric attacks such as rotation and cropping.
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Deng, Tianpeng, Xuan Li, Biao Jin, Lei Chen, and Jie Lin. "Achieving Lightweight Privacy-Preserving Image Sharing and Illegal Distributor Detection in Social IoT." Security and Communication Networks 2021 (June 5, 2021): 1–13. http://dx.doi.org/10.1155/2021/5519558.
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The applications of social Internet of Things (SIoT) with large numbers of intelligent devices provide a novel way for social behaviors. Intelligent devices share images according to the groups of their specified owners. However, sharing images may cause privacy disclosure when the images are illegally distributed without owners’ permission. To tackle this issue, combining blind watermark with additive secret sharing technique, we propose a lightweight and privacy-preserving image sharing (LPIS) scheme with illegal distributor detection in SIoT. Specifically, the query user’s authentication information is embedded in two shares of the transformed encrypted image by using discrete cosine transform (DCT) and additive secret sharing technique. The robustness against attacks, such as JPEG attack and the least significant bit planes (LSBs) replacement attacks, are improved by modifying 1/8 of coefficients of the transformed image. Moreover, we adopt two edge servers to provide image storage and authentication information embedding services for reducing the operational burden of clients. As a result, the identity of the illegal distributor can be confirmed by the watermark extraction of the suspicious image. Finally, we conduct security analysis and ample experiments. The results show that LPIS is secure and robust to prevent illegal distributors from modifying images and manipulating the embedded information before unlawful sharing.
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Shi, Shanshan, Ting Luo, Jiangtao Huang, and Meng Du. "A Novel HDR Image Zero-Watermarking Based on Shift-Invariant Shearlet Transform." Security and Communication Networks 2021 (March 26, 2021): 1–12. http://dx.doi.org/10.1155/2021/6662700.
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In this paper, a novel high dynamic range (HDR) image zero-watermarking algorithm against the tone mapping attack is proposed. In order to extract stable and invariant features for robust zero-watermarking, the shift-invariant shearlet transform (SIST) is used to transform the HDR image. Firstly, the HDR image is converted to CIELAB color space, and the L component is selected to perform SIST for obtaining the low-frequency subband containing the robust structure information of the image. Secondly, the low-frequency subband is divided into nonoverlapping blocks, which are transformed by using discrete cosine transform (DCT) and singular value decomposition (SVD) to obtain the maximum singular values for constructing a binary feature image. To increase the watermarking security, a hybrid chaotic mapping (HCM) is employed to get the scrambled watermark. Finally, an exclusive-or operation is performed between the binary feature image and the scrambled watermark to compute robust zero-watermark. Experimental results show that the proposed algorithm has a good capability of resisting tone mapping and other image processing attacks.
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Moreno-Alvarado, Rodolfo, Eduardo Rivera-Jaramillo, Mariko Nakano, and Hector Perez-Meana. "Simultaneous Audio Encryption and Compression Using Compressive Sensing Techniques." Electronics 9, no. 5 (May 22, 2020): 863. http://dx.doi.org/10.3390/electronics9050863.
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The development of coding schemes with the capacity to simultaneously encrypt and compress audio signals is a subject of active research because of the increasing necessity for transmitting sensitive audio information over insecure communication channels. Thus, several schemes have been developed; firstly, some of them compress the digital information and subsequently encrypt the resulting information. These schemas efficiently compress and encrypt the information. However, they may compromise the information as it can be accessed before encryption. To overcome this problem, a compressing sensing-based system to simultaneously compress and encrypt audio signals is proposed in which the audio signal is segmented in frames of 1024 samples and transformed into a sparse frame using the discrete cosine transform (DCT). Each frame is then multiplied by a different sensing matrix generated using the chaotic mixing scheme. This fact allows that the proposed scheme satisfies the extended Wyner secrecy (EWS) criterion. The evaluation results obtained using several genres of audio signals show that the proposed system allows to simultaneously compress and encrypt audio signals, satisfying the EWS criterion.
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Thakre, Nitin, Sakshi Junghare, Pranali Sakhre, and Dipti Khawse. "Steganography a Technique to Hide Information within an Image." Journal of Cyber Security, Privacy Issues and Challenges 2, no. 1 (April 15, 2023): 14–19. http://dx.doi.org/10.46610/jcspic.2023.v02i01.003.
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Steganography is the science and art of secret communication between two parties that seek to conceal the content of their messages. It is a technology that embeds information into cover photos without losing information after embedding. Steganography is the technique and method of hiding a message so that no one but the sender and receiver will suspect how the message survived. This article compares two different techniques. The first technique uses the least significant bit (LSB) without encryption or compression. Now in the second method, the encrypted message is first encrypted and then the least significant bit is used. The image is then transformed into the frequency domain using a Discrete Cosine Transform (DCT). The LSB algorithm is applied in the local domain where the image parts corresponding to the payload are embedded in the least significant parts of the overlay image and the image is embedded in the least significant parts of the overlay image to generate a steganographic image. The DCT algorithm is appliedin the frequency domain and transforms from the steganographic image domain to the spatial domain where the image title is interpolated to the least significant parts of the image. This white paper mainly provides an overview of steganography requirements benefits and related technologies. This white paper shows which steganographic methods are most effective for which requirements are met and which applications are most compatible with which steganographic methods.
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Choudhury, Bismita, Patrick Then, Biju Issac, Valliappan Raman, and Manas Kumar Haldar. "A Survey on Biometrics and Cancelable Biometrics Systems." International Journal of Image and Graphics 18, no. 01 (January 2018): 1850006. http://dx.doi.org/10.1142/s0219467818500067.
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Now-a-days, biometric systems have replaced the password or token based authentication system in many fields to improve the security level. However, biometric system is also vulnerable to security threats. Unlike password based system, biometric templates cannot be replaced if lost or compromised. To deal with the issue of the compromised biometric template, template protection schemes evolved to make it possible to replace the biometric template. Cancelable biometric is such a template protection scheme that replaces a biometric template when the stored template is stolen or lost. It is a feature domain transformation where a distorted version of a biometric template is generated and matched in the transformed domain. This paper presents a review on the state-of-the-art and analysis of different existing methods of biometric based authentication system and cancelable biometric systems along with an elaborate focus on cancelable biometrics in order to show its advantages over the standard biometric systems through some generalized standards and guidelines acquired from the literature. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation (DCT) and Huffman encoding. We tested and evaluated the proposed novel method for 50 users and achieved good results.
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Sae-Tang, Wannida, and Adisorn Sirikham. "Image Steganography-based Copyright and Privacy-Protected Image Trading Systems." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 17, no. 3 (August 12, 2023): 358–75. http://dx.doi.org/10.37936/ecti-cit.2023173.252500.
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This paper proposes steganography-based copyright- and privacy-protected image trading systems using image transformation, i.e., either discrete cosine transform (DCT) or Hadamard transform (HT). In the systems, there are a content provider (CP), a consumer, the first trusted third party (TTP), and the second TTP. To protect the copyright of the image, the consumer ID is embedded into the amplitude components of the commercial image by the first TTP using the digital fingerprinting technique, and to protect the consumer's privacy against the first TTP and a malicious third party (s), the image steganography is applied to the commercial image by using image transformation. A color dummy image is used instead of a gray dummy image for security purposes. After applying the image transformation to both images, the coefficient signs of the commercial image are replaced by the coefficient signs of the dummy image pixel-by-pixel so that the inversely transformed commercial image looks like the dummy image instead of the commercial image. Once the consumer receives the fingerprinted image from the first TTP and the coefficient signs of the commercial image from the second TTP, the consumer reconstructs the fingerprinted commercial image without losing the hidden fingerprint at all because of the compatibility of the proposed image steganography method and the amplitude-based fingerprinting method. The experimental results confirm that the stego-images generated by the proposed systems do not look suspicious with higher qualities compared with those generated by existing systems. Moreover, the fingerprinted image quality and the correct fingerprint extracting rate have been improved by the proposed systems.
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DISHABI, MOHAMMAD REZA EBRAHIMI, MOHAMMAD ABDOLLAHI AZGOMI, and AMIR MASOUD RAHMANI. "AN IMPROVED TECHNIQUE FOR PRIVACY PRESERVING CLUSTERING BASED ON DAUBECHIES-2 WAVELET TRANSFORM." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 05 (September 2013): 1350039. http://dx.doi.org/10.1142/s0219691313500392.
Full textAbstract:
Having high accuracy results in data clustering and preserving the privacy of data are among the main challenges in privacy preserving clustering (PPC) techniques. High dimensionality of data is another challenge in PPC, which reduces the efficiency of the data mining algorithms. Therefore, PPC algorithms are divided into two categories. The algorithms in the first category protect the data privacy and do not reduce the data dimensionality whereas the algorithms in the second category not only preserve the data privacy but also reduce the data dimensionality. The techniques based on geometric data transformation methods (GTDMs) are related to the first category whereas the techniques based on random projection (RP), discrete cosine transform (DCT) and Haar wavelet transform (HWT) are related to the second category. The GTDMs algorithms do not reduce the data dimensionality. This is the main drawback of this algorithm which causes reduction in the performance of data mining algorithm in large datasets. The technique based on Haar wavelet transform automatically recognizes the dimensionality of the transformed data by using data energy. However, the main problem is the nature of Haar wavelet, which has one vanishing point. In this paper, we show that using Daubechies-2 wavelet, which has two vanishing points, increases the clustering quality. Therefore, to fix the drawback of the PPC algorithm based on Haar wavelet, we introduce a new algorithm to improve both the clustering quality and the privacy measure of data by using Daubechies-2 wavelet transform (D2WT). The results of experiments using several datasets, comparing the new algorithm with other existing techniques, are also presented in this paper.
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Zhao, Huihuang, Jianzhen Chen, Shibiao Xu, Ying Wang, and Zhijun Qiao. "Compressive sensing for noisy solder joint imagery based on convex optimization." Soldering & Surface Mount Technology 28, no. 2 (April 4, 2016): 114–22. http://dx.doi.org/10.1108/ssmt-09-2014-0017.
Full textAbstract:
Purpose The purpose of this paper is to develop a compressive sensing (CS) algorithm for noisy solder joint imagery compression and recovery. A fast gradient-based compressive sensing (FGbCS) approach is proposed based on the convex optimization. The proposed algorithm is able to improve performance in terms of peak signal noise ratio (PSNR) and computational cost. Design/methodology/approach Unlike traditional CS methods, the authors first transformed a noise solder joint image to a sparse signal by a discrete cosine transform (DCT), so that the reconstruction of noisy solder joint imagery is changed to a convex optimization problem. Then, a so-called gradient-based method is utilized for solving the problem. To improve the method efficiency, the authors assume the problem to be convex with the Lipschitz gradient through the replacement of an iteration parameter by the Lipschitz constant. Moreover, a FGbCS algorithm is proposed to recover the noisy solder joint imagery under different parameters. Findings Experiments reveal that the proposed algorithm can achieve better results on PNSR with fewer computational costs than classical algorithms like Orthogonal Matching Pursuit (OMP), Greedy Basis Pursuit (GBP), Subspace Pursuit (SP), Compressive Sampling Matching Pursuit (CoSaMP) and Iterative Re-weighted Least Squares (IRLS). Convergence of the proposed algorithm is with a faster rate O(k*k) instead of O(1/k). Practical implications This paper provides a novel methodology for the CS of noisy solder joint imagery, and the proposed algorithm can also be used in other imagery compression and recovery. Originality/value According to the CS theory, a sparse or compressible signal can be represented by a fewer number of bases than those required by the Nyquist theorem. The new development might provide some fundamental guidelines for noisy imagery compression and recovering.