Journal articles: 'Digital image compression'

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Tiwari, Deepshikha, and Vipin Tyagi. "Digital Image Compression." Indian Science Cruiser 31, no. 6 (November 1, 2017): 44. http://dx.doi.org/10.24906/isc/2017/v31/i6/166460.

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., Gomathi K. V. "DIGITAL IMAGE COMPRESSION TECHNIQUES." International Journal of Research in Engineering and Technology 03, no. 10 (October 25, 2014): 285–90. http://dx.doi.org/10.15623/ijret.2014.0310044.

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Paul, Okuwobi Idowu, and Yong Hua Lu. "A New Approach in Digital Image Compression Using Unequal Error Protection (UEP)." Applied Mechanics and Materials 704 (December 2014): 403–7. http://dx.doi.org/10.4028/www.scientific.net/amm.704.403.

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This paper proposes a new algorithms for compression of digital images especially at the encoding stage of compressive sensing. The research consider the fact that a certain region of a given imagery is more important in most applications. The first algorithm proposed for the encoding stage of Compressive Sensing (CS) exploits the known structure of transform image coefficients. The proposed algorithm makes use of the unequal error protection (UEP) principle, which is widely used in the area of error control coding. The second algorithm which exploits the UEP principle to recover the more important part of an image with more quality while the rest part of the image is not significantly degraded. The proposed algorithm shown to be successful in digital image compression where images are represented in the spatial and transform domains. This new algorithm were recommended for use in image compression.

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Brysina, Iryna Victorivna, and Victor Olexandrovych Makarichev. "DISCRETE ATOMIC COMPRESSION OF DIGITAL IMAGES: ALMOST LOSSLESS COMPRESSION." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 1 (March 23, 2019): 29–36. http://dx.doi.org/10.32620/reks.2019.1.03.

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In this paper, we consider the problem of digital image compression with high requirements to the quality of the result. Obviously, lossless compression algorithms can be applied. Since lossy compression provides a higher compression ratio and, hence, higher memory savings than lossless compression, we propose to use lossy algorithms with settings that provide the smallest loss of quality. The subject matter of this paper is almost lossless compression of full color 24-bit digital images using the discrete atomic compression (DAC) that is an algorithm based on the discrete atomic transform. The goal is to investigate the compression ratio and the quality loss indicators such as uniform (U), root mean square (RMS) and peak signal to noise ratio (PSNR) metrics. We also study the distribution of the difference between pixels of the original image and the corresponding pixels of the reconstructed image. In this research, the classic test images and the classic aerial images are considered. U-metric, which is highly dependent on even minor local changes, is considered as the major metric of quality loss. We solve the following tasks: to evaluate memory savings and loss of quality for each test image. We use the methods of digital image processing, atomic function theory, and approximation theory. The computer program "Discrete Atomic Compression: User Kit" with the mode "Almost Lossless Compression" is used to obtain results of the DAC processing of test images. We obtain the following results: 1) the difference between the smallest and the largest loss of quality is minor; 2) loss of quality is quite stable and predictable; 3) the compression ratio depends on the smoothness of the color change (the smallest and the largest values are obtained when processing the test images with the largest and the smallest number of small details in the image, respectively); 4) DAC provides 59 percent of memory savings; 5) ZIP-compression of DAC-files, which contain images compressed by DAC, is efficient. Conclusions: 1) the almost lossless compression mode of DAC provides sufficiently stable values of the considered quality loss metrics; 2) DAC provides relatively high compression ratio; 3) there is a possibility of further optimization of the DAC algorithm; 4) further research and development of this algorithm are promising.

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Mohammed, Sajaa G., Safa S. Abdul-Jabbar, and Faisel G. Mohammed. "Art Image Compression Based on Lossless LZW Hashing Ciphering Algorithm." Journal of Physics: Conference Series 2114, no. 1 (December 1, 2021): 012080. http://dx.doi.org/10.1088/1742-6596/2114/1/012080.

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Abstract Color image compression is a good way to encode digital images by decreasing the number of bits wanted to supply the image. The main objective is to reduce storage space, reduce transportation costs and maintain good quality. In current research work, a simple effective methodology is proposed for the purpose of compressing color art digital images and obtaining a low bit rate by compressing the matrix resulting from the scalar quantization process (reducing the number of bits from 24 to 8 bits) using displacement coding and then compressing the remainder using the Mabel ZF algorithm Welch LZW. The proposed methodology maintains the quality of the reconstructed image. Macroscopic and quantitative experimental results on technical color images show that the proposed methodology gives reconstructed images with a high PSNR value compared to standard image compression techniques.

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Singh Samra, Hardeep. "Image Compression Techniques." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 2 (April 30, 2012): 49–52. http://dx.doi.org/10.24297/ijct.v2i1.2616.

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Digital images required large number of bits to represent them and in their canonical representation, generally contain significant amount of redundancy. Image compression techniques reduce the number of bits required to represent an image by taking advantage of these redundancies.To overcome this redundancy several image compression techniques are discussed in this paper along with their benefits.

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Wen, Cathlyn Y., and Robert J. Beaton. "Subjective Image Quality Evaluation of Image Compression Techniques." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 23 (October 1996): 1188–92. http://dx.doi.org/10.1177/154193129604002309.

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Image compression reduces the amount of data in digital images and, therefore, allows efficient storage, processing, and transmission of pictorial information. However, compression algorithms can degrade image quality by introducing artifacts, which may be unacceptable for users' tasks. This work examined the subjective effects of JPEG and wavelet compression algorithms on a series of medical images. Six digitized chest images were processed by each algorithm at various compression levels. Twelve radiologists rated the perceived image quality of the compressed images relative to the corresponding uncompressed images, as well as rated the acceptability of the compressed images for diagnostic purposes. The results indicate that subjective image quality and acceptability decreased with increasing compression levels; however, all images remained acceptable for diagnostic purposes. At high compression ratios, JPEG compressed images were judged less acceptable for diagnostic purposes than the wavelet compressed images. These results contribute to emerging system design guidelines for digital imaging workstations.

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Yang, J., and K. Bhattacharya. "Combining Image Compression with Digital Image Correlation." Experimental Mechanics 59, no. 5 (January 18, 2019): 629–42. http://dx.doi.org/10.1007/s11340-018-00459-y.

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Rabie, Tamer. "Color-secure digital image compression." Multimedia Tools and Applications 76, no. 15 (September 14, 2016): 16657–79. http://dx.doi.org/10.1007/s11042-016-3942-9.

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Chaturvedi, Soumya. "Different Type of Image Compression using Various techniques, Highlighting Segmentation based image Compression." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (February 28, 2022): 171–77. http://dx.doi.org/10.22214/ijraset.2022.40207.

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Abstract: Image compression (IC) plays an important part in Digital Image Processing (DIP), it is as well very very essential for effective transmission and storing of images. Image Compression (IC), is basically recusing the size of an image and that too without adjusting the quality of the picture. It is kind of software with records pressure on digital Image. The objective is to lessen reiteration of the picture info for you to be accomplished of store or transmit information in a proficient shape. This paper gives review of kinds of images and its compression strategies. An image, in its genuine form, conveys big extent of data which requiress no longer finest large quantity of memory provisions for its garage but moreover causes difficult transmission over limited bandwidth channel. So, one of the acute factors for picture storage space or transmission over any exchange media is Image Compression. Image Compression makes it possible for increasing file sizes of practicable, storable and communicable dimensions. Keywords: Image Compression; segmentation based image compression component; formatting; Lossless compression; Lossy compression; techniques.

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Meyyappan, Thamarai, and Jeya Nachiaban. "Lossless Digital Image Compression Method for Bitmap Images." International journal of Multimedia & Its Applications 3, no. 4 (November 30, 2011): 83–92. http://dx.doi.org/10.5121/ijma.2011.3407.

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Santo, Rafael do Espírito. "Principal Component Analysis applied to digital image compression." Einstein (São Paulo) 10, no. 2 (June 2012): 135–39. http://dx.doi.org/10.1590/s1679-45082012000200004.

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OBJECTIVE: To describe the use of a statistical tool (Principal Component Analysis – PCA) for the recognition of patterns and compression, applying these concepts to digital images used in Medicine. METHODS: The description of Principal Component Analysis is made by means of the explanation of eigenvalues and eigenvectors of a matrix. This concept is presented on a digital image collected in the clinical routine of a hospital, based on the functional aspects of a matrix. The analysis of potential for recovery of the original image was made in terms of the rate of compression obtained. RESULTS: The compressed medical images maintain the principal characteristics until approximately one-fourth of their original size, highlighting the use of Principal Component Analysis as a tool for image compression. Secondarily, the parameter obtained may reflect the complexity and potentially, the texture of the original image. CONCLUSION: The quantity of principal components used in the compression influences the recovery of the original image from the final (compacted) image.

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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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Sirota, A. A., M. A. Dryuchenko, and E. Yu Mitrofanova. "Digital watermarking method based on heteroassociative image compression and its realization with artificial neural networks." Computer Optics 42, no. 3 (July 25, 2018): 483–94. http://dx.doi.org/10.18287/2412-6179-2018-42-3-483-494.

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In this paper, we present a digital watermarking method and associated algorithms that use a heteroassociative compressive transformation to embed a digital watermark bit sequence into blocks (fragments) of container images. A principal feature of the proposed method is the use of the heteroassociative compressing transformation – a mutual mapping with the compression of two neighboring image regions of an arbitrary shape. We also present the results of our experiments, namely the dependencies of quality indicators of thus created digital watermarks, which show the container distortion level, and the probability of digital watermark extraction error. In the final section, we analyze the performance of the proposed digital watermarking algorithms under various distortions and transformations aimed at destroying the hidden data, and compare these algorithms with the existing ones.

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Chang, Yu Hong. "Based on Digital Image Forensics Double JPEG Compression Algorithm." Advanced Materials Research 1044-1045 (October 2014): 1098–101. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1098.

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Means of digital image tampering diverse, new technical approach increasingly renovation, therefore, the detection of forged digital tampering issues are complex, and must not universal, once and for all solutions. In this paper, digital image tampering common practices through research, proposed a re-deposit operations for double JPEG image processing software compression testing technology method using image compression and decompression process lossy compression of images tamper with forensic, this method can play an effective role in the detection.

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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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Pal, Rita. "APPLICATION OF FIXED POINT THEOREM FOR DIGITAL IMAGES." International Journal of Advanced Research 10, no. 01 (January 31, 2022): 1110–26. http://dx.doi.org/10.21474/ijar01/14152.

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In this research paper, we prove some fixed point theorems for digital images. The papers main goal is to present another generalisation of the well-known Banach contraction principle for digital images. The fundamental concepts of digital images are discussed, as is an application of the fixed point theorem to digital image compression and fractal image compression. The digital contractive type mappings in digital metric space are introduced, and the uniqueness of fixed points in digital metric space is proved.

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Fathahillah, Fathahillah, Satria Gunawan Zain, and Rismawati Rismawati. "Homogeneous Image Compression Techniques with the Shannon-Fano Algorithm." International Journal of Environment, Engineering and Education 1, no. 2 (August 28, 2019): 59–66. http://dx.doi.org/10.55151/ijeedu.v1i2.17.

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Compression is a field that needed at this time where increasingly high-tech digital and computing makes it possible to process data in a size that is large enough like multimedia. Compression needed to keep pressing the storage media consumption of data and information stored on computer media. The Shannon-Fano compression algorithm is one of the well-known compression algorithms and is useful in saving data storage space. Shannon-Fano compression algorithms can be performed on text and digital images. In this study applying the Shannon-Fano method to homogeneous digital images using applications created with the MATLAB Program. The product of this research will be tested using several images taken using a webcam. The product tested to find out whether the coding used is correct or has errors. The product will be successful if it can reduce the size of the compressed image from the original image. Homogeneous images that have tested using the Shannon-Fano image compression application have an average compression ratio value of 52%. The size of the compression results reaches half of the original image. The Shannon-Fano compression algorithm detects the number of times that characters appear in each experiment, then the coding of the frequency of characters appearing in binary numbers.

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Stamm, Matthew C., and K. J. Ray Liu. "Anti-forensics of digital image compression." IEEE Transactions on Information Forensics and Security 6, no. 3 (September 2011): 1050–65. http://dx.doi.org/10.1109/tifs.2011.2119314.

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Braner, Dana, and Barry Markovitz. "Digital Image Compression: Pixels n' Bits." Journal of Intensive Care Medicine 18, no. 4 (July 2003): 229–30. http://dx.doi.org/10.1177/0885066603254290.

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Lu, Guojun. "Advances in digital image compression techniques." Computer Communications 16, no. 4 (April 1993): 202–14. http://dx.doi.org/10.1016/0140-3664(93)90090-f.

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Hsieh, Ping Ang, and Ja-Ling Wu. "A Review of the Asymmetric Numeral System and Its Applications to Digital Images." Entropy 24, no. 3 (March 7, 2022): 375. http://dx.doi.org/10.3390/e24030375.

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The Asymmetric Numeral System (ANS) is a new entropy compression method that the industry has highly valued in recent years. ANS is valued by the industry precisely because it captures the benefits of both Huffman Coding and Arithmetic Coding. Surprisingly, compared with Huffman and Arithmetic coding, systematic descriptions of ANS are relatively rare. In 2017, JPEG proposed a new image compression standard—JPEG XL, which uses ANS as its entropy compression method. This fact implies that the ANS technique is mature and will play a kernel role in compressing digital images. However, because the realization of ANS involves combination optimization and the process is not unique, only a few members in the compression academia community and the domestic industry have noticed the progress of this powerful entropy compression approach. Therefore, we think a thorough overview of ANS is beneficial, and this idea brings our contributions to the first part of this work. In addition to providing compact representations, ANS has the following prominent feature: just like its Arithmetic Coding counterpart, ANS has Chaos characteristics. The chaotic behavior of ANS is reflected in two aspects. The first one is that the corresponding compressed output will change a lot if there is a tiny change in the original input; moreover, the reverse is also applied. The second is that ANS compressing an image will produce two intertwined outcomes: a positive integer (aka. state) and a bitstream segment. Correct ANS decompression is possible only when both can be precisely obtained. Combining these two characteristics helps process digital images, e.g., art collection images and medical images, to achieve compression and encryption simultaneously. In the second part of this work, we explore the characteristics of ANS in depth and develop its applications specific to joint compression and encryption of digital images.

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Andrian, Andrian. "Perancangan Perangkat Lunak Kompresi Citra Menggunakan Transformasi Wavelet dan PCA." ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA 3, no. 1 (April 30, 2019): 1. http://dx.doi.org/10.30829/algoritma.v3i1.4430.

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<p>Now days, The most needed of digital images is influenced by the people will that want to take a part of moment life into digital image. The good digital image has the big filesize, so it will need more space memory to saving more images. There is technique in image processing to decrease file size that is compression. By combine wavelet transformation method and Principal Component Analysis in developing application can produce the good compression technique.</p>

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Matsuoka, R. "Effect of Lossy JPEG Compression of an Image with Chromatic Aberrations on Target Measurement Accuracy." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-5 (May 28, 2014): 235–42. http://dx.doi.org/10.5194/isprsannals-ii-5-235-2014.

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This paper reports an experiment conducted to investigate the effect of lossy JPEG compression of an image with chromatic aberrations on the measurement accuracy of target center by the intensity-weighted centroid method. I utilized six images shooting a white sheet with 30 by 20 black filled circles in the experiment. The images were acquired by a digital camera Canon EOS 20D. The image data were compressed by using two compression parameter sets of a downsampling ratio, a quantization table and a Huffman code table utilized in EOS 20D. The experiment results clearly indicate that lossy JPEG compression of an image with chromatic aberrations would produce a significant effect on measurement accuracy of target center by the intensity-weighted centroid method. The maximum displacements of red, green and blue components caused by lossy JPEG compression were 0.20, 0.09, and 0.20 pixels respectively. The results also suggest that the downsampling of the chrominance components Cb and Cr in lossy JPEG compression would produce displacements between uncompressed image data and compressed image data. In conclusion, since the author consider that it would be unable to correct displacements caused by lossy JPEG compression, the author would recommend that lossy JPEG compression before recording an image in a digital camera should not be executed in case of highly precise image measurement by using color images acquired by a non-metric digital camera.

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Basha, Shaik Mahaboob, and B. C. Jinaga. "A Novel Optimized Golomb-Rice Technique for the Reconstruction in Lossless Compression of Digital Images." ISRN Signal Processing 2013 (August 7, 2013): 1–5. http://dx.doi.org/10.1155/2013/539759.

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The research trends that are available in the area of image compression for various imaging applications are not adequate for some of the applications. These applications require good visual quality in processing. In general the tradeoff between compression efficiency and picture quality is the most important parameter to validate the work. The existing algorithms for still image compression were developed by considering the compression efficiency parameter by giving least importance to the visual quality in processing. Hence, we proposed a novel lossless image compression algorithm based on Golomb-Rice coding which was efficiently suited for various types of digital images. Thus, in this work, we specifically address the following problem that is to maintain the compression ratio for better visual quality in the reconstruction and considerable gain in the values of peak signal-to-noise ratios (PSNR). We considered medical images, satellite extracted images, and natural images for the inspection and proposed a novel technique to increase the visual quality of the reconstructed image.

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Rajamanickam, Kousalyadevi, and J. Suganthi. "Performance analysis of landsat5 remote sensing data compression technique used for land use and land cover mapping." International Journal of Engineering & Technology 3, no. 3 (June 20, 2014): 308. http://dx.doi.org/10.14419/ijet.v3i3.2788.

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Multispectral band remote sensing imagery is used for environmental monitoring and land use and land cover mapping purposes. This image contains huge volume of data. Instead of using the entire data for land use land cove mapping, the spatially compressed images can also be used for mapping purposes. In this paper discrete wavelet transform is selected for compressing the Landsat5 image and the quality has been analysed using the parameters compression ratio, peak signal to noise ratio and digital number values. Using the digital number values the spectral signature graph is drawn. Finally only one wavelet is selected for land use and land cover mapping based on minimum cumulative error of the digital number values. Then the selected wavelet compressed image is classified using supervised classification technique and accuracy assessment is made by constructing the error matrix. Finally the selected wavelet compressed image is used for land use and land cover mapping. Keywords: Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR), Digital Number (DN), Image Classification, Error Matrix, Spectral Signatures.

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Kutsenko, Svetlana Anatolievna, Svetlana Rafailovna Nemtsova, Aleksandr Sergeevich Mkrtumov, and Oleg Viktorovich Tsvetkov. "Information-theoretical image quality metric in digital image compression." SPIIRAS Proceedings 8, no. 31 (April 2, 2014): 190. http://dx.doi.org/10.15622/sp.31.11.

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Abdul, Wadood. "Region Based Segmentation Techniques for Digital Images." Journal of Computational and Theoretical Nanoscience 16, no. 9 (September 1, 2019): 3792–801. http://dx.doi.org/10.1166/jctn.2019.8252.

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This paper discusses region based segmentation techniques for digital images. For a few applications, such as image compression or recognition, we cannot handle the entire picture straightforwardly as it is unconventional and inefficient. Due to these reasons, many algorithms related to image segmentation are proposed in the literature to divide an image prior to compression or recognition. The segmentation of an image is basically done to arrange or group the image in a few fragments (districts) as specified by the elements of an image, for instance, according to the value of pixel or frequency response. Currently, many image segmentation approaches exist and are widely used in across scientific disciplines and daily human life. The segmentation approaches can be generally categorized to segmentation based on region, segmentation based on edges, and information grouping.

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Amri, Hedi, Ali Khalfallah, Malek Gargouri, Naïma Nebhani, Jean-Christophe Lapayre, and Mohamed-Salim Bouhlel. "Medical Image Compression Approach Based on Image Resizing, Digital Watermarking and Lossless Compression." Journal of Signal Processing Systems 87, no. 2 (July 23, 2016): 203–14. http://dx.doi.org/10.1007/s11265-016-1150-5.

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Cahya Dewi, Dewa Ayu Indah, and I. Made Oka Widyantara. "Usage analysis of SVD, DWT and JPEG compression methods for image compression." Jurnal Ilmu Komputer 14, no. 2 (September 30, 2021): 99. http://dx.doi.org/10.24843/jik.2021.v14.i02.p04.

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Through image compression, can save bandwidth usage on telecommunication networks, accelerate image file sending time and can save memory in image file storage. Technique to reduce image size through compression techniques is needed. Image compression is one of the image processing techniques performed on digital images with the aim of reducing the redundancy of the data contained in the image so that it can be stored or transmitted efficiently. This research analyzed the results of image compression and measure the error level of the image compression results. The analysis to be carried out is in the form of an analysis of JPEG compression techniques with various types of images. The method of measuring the compression results uses the MSE and PSNR methods. Meanwhile, to determine the percentage level of compression using the compression ratio calculation. The average ratio for JPEG compression was 0.08605, the compression rate was 91.39%. The average compression ratio for the DWT method was 0.133090833, the compression rate was 86.69%. The average compression ratio of the SVD method was 0.101938833 and the compression rate was 89.80%.

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M, Anand, and V. Mathivananr. "Quantization Encoding Algorithm Based Satellite Image Compression." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 3 (December 1, 2017): 740. http://dx.doi.org/10.11591/ijeecs.v8.i3.pp740-742.

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In the field of digital data there is a demand in bandwidth for the transmission of the videos and images all over the worlds. So in order to reduce the storage space in the field of image applications there is need for the image compression process with lesser transmission bandwidth. So in this paper we are proposing a new image compression technique for the compression of the satellite images by using the Region of Interest (ROI) based on the lossy image technique called the Quantization encoding algorithm for the compression. The performance of our method can be evaluated and analyzing the PSNR values of the output images.

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DUDHAGARA, CHETAN R., and MAYUR M. PATEL. "A Comparative Study and Analysis of EZW and SPIHT methods for Wavelet based Image Compression." Oriental journal of computer science and technology 10, no. 3 (August 10, 2017): 669–73. http://dx.doi.org/10.13005/ojcst/10.03.17.

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In recent years there has been widely increase the use of digital media everywhere. To increase the use of digital media, there is a huge problem of storage, manipulation and transmission of data over the internet. These digital media such as image, audio and video require large memory space. So it is necessary to compress the digital data to require less memory space and less bandwidth to transmission of data over network. Image compressions techniques are used to compress the data for reduce the storage requirement. It plays an important role for transfer of data such as image over the network. Two methods are used in this paper on Barbara image. This compression study is performed by using Set Partitioning In Hierarchical Trees (SPIHT) and Embedded Zero tree Wavelet (EZW) compression techniques. There are many parameters are used to compare this techniques. Mean Square Error (MSE), Pick Signal to Noise Ration (PSNR) and Compression Ratio (CR) are used at different level of decompositions.

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Anandita, Ida Bagus Gede, I. Gede Aris Gunadi, and Gede Indrawan. "Analisis Kinerja Dan Kualitas Hasil Kompresi Pada Citra Medis Sinar-X Menggunakan Algoritma Huffman, Lempel Ziv Welch Dan Run Length Encoding." SINTECH (Science and Information Technology) Journal 1, no. 1 (February 9, 2018): 7–15. http://dx.doi.org/10.31598/sintechjournal.v1i1.179.

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Technological progress in the medical area made medical images like X-rays stored in digital files. The medical image file is relatively large so that the image needs to be compressed. The lossless compression technique is an image compression where the decompression results are the same as the original or no information lost in the compression process. The existing algorithms on lossless compression techniques are Run Length Encoding (RLE), Huffman, and Lempel Ziv Welch (LZW). This study compared the performance of the three algorithms in compressing medical images. The result of image decompression will be compared to its performance in the objective assessment such as ratio, compression time, MSE (Mean Square Error) and PNSR (Peak Signal to Noise Ratio). MSE and PSNR are used for quantitative image quality measurement for subjective assessment assisted by three experts who will compare the original image with the decompression image. Based on the results obtained from the objective assessment of compression performance of RLE algorithm showed the best performance by yielding ratio, time, MSE and PSNR respectively 86,92%, 3,11ms, 0 and 0db. For Huffman, the results can be 12.26%, 96.94ms, 0, and 0db respectively. While LZW results can be in sequence -63.79%, 160ms, 0.3 and 58.955db. For the results of the subjective assessment, the experts argued that all images can be analyzed well.

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Younes, Ghada. "Digital Image Compression using Karhunen-Loève Transform." AL-Rafidain Journal of Computer Sciences and Mathematics 10, no. 3 (September 1, 2013): 47–59. http://dx.doi.org/10.33899/csmj.2013.163535.

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Widjaja, Joewono. "Effects of image compression on digital specklegrams." Optics and Lasers in Engineering 39, no. 4 (April 2003): 501–6. http://dx.doi.org/10.1016/s0143-8166(02)00032-5.

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Takeuchi, Yashinori, and Masayasu Ito. "Recent Technology for Digital Image Compression Alogorithm." IEEJ Transactions on Electronics, Information and Systems 114, no. 7-8 (1994): 767–72. http://dx.doi.org/10.1541/ieejeiss1987.114.7-8_767.

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Sulttan, Bushra A. "DIGITAL IMAGE COMPRESSION USING INTEGER BASEDTRANSFORM ALGORITHM." Journal of Al-Nahrain University Science 12, no. 3 (September 1, 2009): 177–83. http://dx.doi.org/10.22401/jnus.12.3.25.

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Guarneri, F., M. Vaccaro, and C. Guarneri. "Digital Image Compression in Dermatology: Format Comparison." Telemedicine and e-Health 14, no. 7 (September 2008): 666–70. http://dx.doi.org/10.1089/tmj.2007.0119.

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Rabie, Tamer. "Erratum to: Color-secure digital image compression." Multimedia Tools and Applications 76, no. 15 (October 17, 2016): 16681. http://dx.doi.org/10.1007/s11042-016-4028-4.

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Yimin, Cheng, Wang Yixiao, Sun Qibin, and Sun Longxiang. "Digital Image Compression Using a Genetic Algorithm." Real-Time Imaging 5, no. 6 (December 1999): 379–83. http://dx.doi.org/10.1006/rtim.1998.0133.

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Li, Zhen, Changgen Peng, Weijie Tan, and Liangrong Li. "An Efficient Plaintext-Related Chaotic Image Encryption Scheme Based on Compressive Sensing." Sensors 21, no. 3 (January 23, 2021): 758. http://dx.doi.org/10.3390/s21030758.

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With the development of mobile communication network, especially 5G today and 6G in the future, the security and privacy of digital images are important in network applications. Meanwhile, high resolution images will take up a lot of bandwidth and storage space in the cloud applications. Facing the demands, an efficient and secure plaintext-related chaotic image encryption scheme is proposed based on compressive sensing for achieving the compression and encryption simultaneously. In the proposed scheme, the internal keys for controlling the whole process of compression and encryption is first generated by plain image and initial key. Subsequently, discrete wavelets transform is used in order to convert the plain image to the coefficient matrix. After that, the permutation processing, which is controlled by the two-dimensional Sine improved Logistic iterative chaotic map (2D-SLIM), was done on the coefficient matrix in order to make the matrix energy dispersive. Furthermore, a plaintext related compressive sensing has been done utilizing a measurement matrix generated by 2D-SLIM. In order to make the cipher image lower correlation and distribute uniform, measurement results quantified the 0∼255 and the permutation and diffusion operation is done under the controlling by two-dimensional Logistic-Sine-coupling map (2D-LSCM). Finally, some common compression and security performance analysis methods are used to test our scheme. The test and comparison results shown in our proposed scheme have both excellent security and compression performance when compared with other recent works, thus ensuring the digital image application in the network.

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Kikuchi, Tsutomu, Hidekazu Tsubota, Tomoyasu Tsuzuki, Hidekazu Fujii, and Tsukasa Sasaki. "99. Digital Image Restoration of High-rate Compression Degraded Images." Japanese Journal of Radiological Technology 47, no. 2 (1991): 210. http://dx.doi.org/10.6009/jjrt.kj00003322885.

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Karson, Tom H., Shalabh Chandra, Annitta J. Morehead, William J. Stewart, Steven E. Nissen, and James D. Thomas. "JPEG compression of digital echocardiographic images: Impact on image quality." Journal of the American Society of Echocardiography 8, no. 3 (May 1995): 306–18. http://dx.doi.org/10.1016/s0894-7317(05)80041-0.

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Grgić, Mislav, Sonja Grgić, and Branka Zovko-Cihlar. "DCTlab: Educational Software for Still Image Compression and its Application in a Digital Television Course." International Journal of Electrical Engineering & Education 38, no. 3 (July 2001): 187–98. http://dx.doi.org/10.7227/ijeee.38.3.1.

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Current standards for the compression of still and moving images use Discrete Cosine Transform (DCT) to remove spatial redundancy in images. Students specialising in image and video system engineering need to know why DCT is important in their field of interest and to understand the influence of DCT-based image compression on picture quality. Therefore, we have developed educational software, called DCTlab, that helps students to analyse DCT application in still image compression systems. This paper describes software characteristics, its application in a digital television course and learning outcomes.

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Chakib, Houda, Brahim Minaoui, Abderrahim Salhi, and Imad Badi. "Switching of Wavelet Transforms by Neural Network for Image Compression." Journal of Electronic Commerce in Organizations 16, no. 1 (January 2018): 43–56. http://dx.doi.org/10.4018/jeco.2018010104.

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Nowadays, digital images compression requires more and more significant attention of researchers. Even when high data rates are available, image compression is necessary in order to reduce the memory used, as well the transmission cost. An ideal image compression system must yield high-quality compressed image with high compression ratio. In this article, a neural network is implemented for image compression using the feature of wavelet transform. The idea is that a back-propagation neural network can be trained to relate the image contents to its ideal compression method between two different wavelet transforms: orthogonal (Haar) and biorthogonal (bior4.4).

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Jiang, W., S. Chen, X. Wang, Q. Huang, H. Shi, and Y. Man. "REMOTE SENSING IMAGE QUALITY ASSESSMENT EXPERIMENT WITH POST-PROCESSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 665–68. http://dx.doi.org/10.5194/isprs-archives-xlii-3-665-2018.

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This paper briefly describes the post-processing influence assessment experiment, the experiment includes three steps: the physical simulation, image processing, and image quality assessment. The physical simulation models sampled imaging system in laboratory, the imaging system parameters are tested, the digital image serving as image processing input are produced by this imaging system with the same imaging system parameters. The gathered optical sampled images with the tested imaging parameters are processed by 3 digital image processes, including calibration pre-processing, lossy compression with different compression ratio and image post-processing with different core. Image quality assessment method used is just noticeable difference (JND) subject assessment based on ISO20462, through subject assessment of the gathered and processing images, the influence of different imaging parameters and post-processing to image quality can be found. The six JND subject assessment experimental data can be validated each other. Main conclusions include: image post-processing can improve image quality; image post-processing can improve image quality even with lossy compression, image quality with higher compression ratio improves less than lower ratio; with our image post-processing method, image quality is better, when camera MTF being within a small range.

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Kavitha, T., and K. Jayasankar. "Ideal Huffman Code for Lossless Image Compression for Ubiquitous Access." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (November 1, 2018): 765. http://dx.doi.org/10.11591/ijeecs.v12.i2.pp765-774.

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<p>Compression technique is adopted to solve various big data problems such as storage and transmission. The growth of cloud computing and smart phone industries has led to generation of huge volume of digital data. Digital data can be in various forms as audio, video, images and documents. These digital data are generally compressed and stored in cloud storage environment. Efficient storing and retrieval mechanism of digital data by adopting good compression technique will result in reducing cost. The compression technique is composed of lossy and lossless compression technique. Here we consider Lossless image compression technique, minimizing the number of bits for encoding will aid in improving the coding efficiency and high compression. Fixed length coding cannot assure in minimizing bit length. In order to minimize the bits variable Length codes with prefix-free codes nature are preferred. However the existing compression model presented induce high computing overhead, to address this issue, this work presents an ideal and efficient modified Huffman technique that improves compression factor up to 33.44% for Bi-level images and 32.578% for Half-tone Images. The average computation time both encoding and decoding shows an improvement of 20.73% for Bi-level images and 28.71% for Half-tone images. The proposed work has achieved overall 2% increase in coding efficiency, reduced memory usage of 0.435% for Bi-level images and 0.19% for Half-tone Images. The overall result achieved shows that the proposed model can be adopted to support ubiquitous access to digital data.</p>

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Wang, Qing Jun, and Xiao Bin Wang. "Digital Image Watermarking Program." Advanced Materials Research 717 (July 2013): 720–25. http://dx.doi.org/10.4028/www.scientific.net/amr.717.720.

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This paper proposes digital image watermarking algorithm in DCT domain which makes use of the unique nature of chaotic sequences to encrypt binary image watermarking with copyright information via chaotic sequences and then embeds watermarking component of different strengths in DCT domain of different image blocks self-adaptively. Through the added Gaussian noise, salt and pepper noise and JPEG compression and shear processing operations, the results show that the algorithm has a good visual masking and robustness.

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Brysina, Iryna Victorivna, and Victor Olexandrovych Makarichev. "DISCRETE ATOMIC COMPRESSION OF DIGITAL IMAGES." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 4 (December 20, 2018): 17–33. http://dx.doi.org/10.32620/reks.2018.4.02.

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The subject matter of this paper is the discrete atomic compression (DAC) of digital images, which is a lossy compression process based on the discrete atomic transform (DAT). The goal is to investigate the efficiency of the DAC algorithm. We solve the following tasks: to develop a general compression scheme using discrete atomic transform and to compare the results of DAC and JPEG algorithms. In this article, we use the methods of digital image processing, atomic function theory, and approximation theory. To compare the efficiency of DAC with the JPEG compression algorithm we use the sets of the classic test images and the classic aerial images. We analyze compression ratio (CR) and loss of quality, using uniform (U), root mean square (RMS) and peak signal to noise ratio (PSNR) metrics. DAC is an algorithm with flexible parameters. In this paper, we use “Optimal” and “Allowable” modes of this algorithm and compare them with the corresponding modes of JPEG. We obtain the following results: 1) DAC is much better than JPEG by the U-criterion of quality loss; 2) there are no significant differences between DAC and JPEG by RMS and PSNR criterions; 3) the compression ratio of DAC is much higher than the compression ratio of JPEG. In other words, the DAC algorithm saves more memory than the JPEG compression algorithm with not worse quality results. These results are due to the fundamental properties of atomic functions such as good approximation properties, the high order of smoothness and existence of locally supported basis in the spaces of atomic functions. Since generalized Fup-functions have the same convenient properties, it is clear that such compression results can be achieved by application of a generalized discrete atomic transform, which is based on these functions. We also discuss the obtained results in the terms of approximation theory and function theory. Conclusions: 1) it is possible to achieve better results with DAC than with JPEG; 2) application of DAC to image compression is more preferable than JPEG in the case when it is planned to use recognition algorithms; 3) further development and investigation of the DAC algorithm are promising

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Tengowski, Mark W. "Image Compression in Morphometry Studies Requiring 21 CFR Part 11 Compliance: Procedure Is Key with TIFFs and Various JPEG Compression Strengths." Toxicologic Pathology 32, no. 2 (February 2004): 258–63. http://dx.doi.org/10.1080/01926230490274399.

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This study aims to compare the integrity and reproducibility of measurements created from uncompressed and compressed digital images in order to implement compliance with 21 CFR Part 11 for image analysis studies executed using 21 CFR Part 58 compliant capture systems. Images of a 400-mesh electron microscope grid and H&E stained rat liver tissue were captured on an upright microscope with digital camera using commercially available analysis software. Digital images were stored as either uncompressed TIFFs or in one of five different levels of JPEG compression. The grid images were analyzed with automatic detection of bright objects while the liver images were segmented using color cube-based morphometry techniques, respectively, using commercially-available image analysis software? When comparing the feature-extracted measurements from the TIFF uncompressed to the JPEG compressed images, the data suggest that JPEG compression does not alter the accuracy or reliability to reproduce individual data point measurements in all but the highest compression levels. There is, however, discordance if the initial measure was obtained with a TIFF format and subsequently saved as one of the JPEG levels, suggesting that the use of compression must precede feature extraction. It is a common practice in software packages to work with TIFF uncompressed images. However, this study suggests that the use of JPEG compression as part of the analysis work flow was an acceptable practice for these images and features. Investigators applying image file compression to other organ images will need to validate the utility of image compression in their work flow. A procedure to digitally acquire and JPEG compress images prior to image analysis has the potential to reduce file archiving demands without compromising reproducibility of data.

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Journal articles on the topic 'Digital image compression'

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