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Journal articles on the topic 'Image coding'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Saudagar, Abdul Khader Jilani. "Biomedical Image Compression Techniques for Clinical Image Processing." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 12 (2020): 133. http://dx.doi.org/10.3991/ijoe.v16i12.17019.

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Image processing is widely used in the domain of biomedical engineering especially for compression of clinical images. Clinical diagnosis receives high importance which involves handling patient’s data more accurately and wisely when treating patients remotely. Many researchers proposed different methods for compression of medical images using Artificial Intelligence techniques. Developing efficient automated systems for compression of medical images in telemedicine is the focal point in this paper. Three major approaches were proposed here for medical image compression. They are image compres

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V., Yaswanth Varma *. T. Nalini Prasad N. V. Phani Sai Kumar. "IMAGE COMPRESSION METHODS BASED ON TRANSFORM CODING AND FRACTAL CODING." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 10 (2017): 481–87. https://doi.org/10.5281/zenodo.1036337.

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Image compression is process to remove the redundant information from the image so that only essential information can be stored to reduce the storage size, transmission bandwidth and transmission time. The essential information is extracted by various transforms techniques such that it can be reconstructed without losing quality and information of the image. In this research comparative analysis of image compression is done by four transform method, which are Discrete Cosine Transform (DCT), Discrete Wavelet Transform( DWT) & Hybrid (DCT+DWT) Transform and fractal coding. MATLAB programs

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Shaikh, A. A., and P. P. Gadekar. "Huffman Coding Technique for Image Compression." COMPUSOFT: An International Journal of Advanced Computer Technology 04, no. 04 (2015): 1585–87. https://doi.org/10.5281/zenodo.14771964.

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Image compression is one of the most important steps in image transmission and storage. “A picture is worth more than thousand words “is a common saying. Images play an indispensable role in representing vitalin formation and needs to be saved for further use or can be transmitted over a medium. In order to have efficient utilization of disk space and transmission rate, images need to be compressed. Image compression is the technique of reducing the file size of a image without compromising with the image quality at acceptable level. Image compression is been used from a long time

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Takezawa, Takuma, and Yukihiko Yamashita. "Wavelet Based Image Coding via Image Component Prediction Using Neural Networks." International Journal of Machine Learning and Computing 11, no. 2 (2021): 137–42. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1026.

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In the process of wavelet based image coding, it is possible to enhance the performance by applying prediction. However, it is difficult to apply the prediction using a decoded image to the 2D DWT which is used in JPEG2000 because the decoded pixels are apart from pixels which should be predicted. Therefore, not images but DWT coefficients have been predicted. To solve this problem, predictive coding is applied for one-dimensional transform part in 2D DWT. Zhou and Yamashita proposed to use half-pixel line segment matching for the prediction of wavelet based image coding with prediction. In th

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Suliman Ali Bakouri, Anis. "TIFF Image Compression through Huffman Coding Technique." International Journal of Science and Research (IJSR) 11, no. 10 (2022): 277–79. http://dx.doi.org/10.21275/sr22929233828.

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Dr., M. Vadivukarassi, and G. JawaherlalNehru Dr. "Efficient Content Based Image Retrieval Analysis of Distance Matrices." IJCSET MAY Volume 9 Issue 5 9, no. 5 (2023): 1–4. https://doi.org/10.5281/zenodo.8382651.

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In this paper, we proposed a new method of feature extraction to improve the efficiency for retrieving the JPEG Compressed Images. We extract two DCT features, namely DC feature and AC feature, from the compressed image. Then we measure the image distance between the query image and the images in the database using these DCT features. Our retrieval system will give rank to the retrieved database images to define its similarity with the query image. Our proposed system does not need to full decoding, it only needs partial entropy decoding. Therefore, our proposed system takes less time for retr

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Arya, G. S*1 &. Shiny C*2. "VARIOUS IMAGE COMPRESSION TECHNIQUES: A REVIEW." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY AICT 2019 (April 4, 2019): 66–71. https://doi.org/10.5281/zenodo.2629272.

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Due to increasing demand for multimedia content such as digital images and video has led to interest in research into compression techniques. The development of higher quality and less expensive image acquisition devices has produced steady growth in both image size and resolution, which tends to greater consequent for the design of efficient compression systems. Therefore, one of the important factors for transmission or storage image data through a communication media is image compression. In this paper we discussed different compression techniques based on transform coding, fractal coding a

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Tanaka, Midori, Tomoyuki Takanashi, and Takahiko Horiuchi. "Glossiness-aware Image Coding in JPEG Framework." Journal of Imaging Science and Technology 64, no. 5 (2020): 50409–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.5.050409.

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Abstract In images, the representation of glossiness, translucency, and roughness of material objects (Shitsukan) is essential for realistic image reproduction. To date, image coding has been developed considering various indices of the quality of the encoded image, for example, the peak signal-to-noise ratio. Consequently, image coding methods that preserve subjective impressions of qualities such as Shitsukan have not been studied. In this study, the authors focus on the property of glossiness and propose a method of glossiness-aware image coding. Their purpose is to develop an encoding algo

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M.A.P., Manimekalai. "Efficient Image Compression Using Improved Huffman Coding With Enhanced Lempel ZIV CODING Approach." Journal of Advanced Research in Dynamical and Control Systems 12, no. 01-Special Issue (2020): 359–68. http://dx.doi.org/10.5373/jardcs/v12sp1/20201082.

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Pearlman, William A., and Amir Said. "Image Wavelet Coding Systems: Part II of Set Partition Coding and Image Wavelet Coding Systems." Foundations and Trends® in Signal Processing 2, no. 3 (2007): 181–246. http://dx.doi.org/10.1561/2000000014.

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Sheng, Zhong, Xiao Yu Jiang, and Wei Zhen. "Pseudo-Color Coding with Phase-Modulated Image Density." Advanced Materials Research 403-408 (November 2011): 1618–21. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1618.

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Traditional pseudo color coding for gray images based on image enhancement technique cannot adequately deal with some of the details information of the image. In this paper, an enhanced approach of peudo-color coding with phase-modulated image density is presented. This method has distinct levels, richer colours, and is adequate for human perception of color ,providing a better algorithm for pseudo color coding based on gray images. This method has great potential in research and application and characteristic of strong universal usage, which can process high gray resolution image.

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Li, Ren Chong, Yi Long You, and Feng Xiang You. "Research of Image Processing Based on Lifting Wavelet Transform." Applied Mechanics and Materials 263-266 (December 2012): 2502–9. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2502.

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This paper Study problems which based on lifting wavelet transform image processing. Coding and decoding a complete digital image by using W97-2 wavelet basis wavelet transform, combined with the embedded zerotree wavelet coding and binary arithmetic coding, and complete a lossless compression combined with the international standard test images. Experimental results show that graphics, image processing will come into a higher level because of wavelet analysis combined with image processing.

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Kumar, Vikas. "Compression Techniques Vs Huffman Coding." International Journal of Informatics and Communication Technology (IJ-ICT) 4, no. 1 (2015): 29. http://dx.doi.org/10.11591/ijict.v4i1.pp29-37.

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<p>The technique for compressioning the Images has been increasing because the fresh images need large amounts of disk space. It is seems to be a big disadvantage during transmission &amp; storage of image. Even though there are so many compression technique already presents and have better technique which is faster, memory efficient and simple, and friendly with the requirements of the user. In this paper we proposed the method for image compression and decompression using a simple coding technique called Huffman coding and show why this is more efficient then other technique. This

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Ibáñez-Berganza, Miguel, Carlo Lucibello, Luca Mariani, and Giovanni Pezzulo. "Information-theoretical analysis of the neural code for decoupled face representation." PLOS ONE 19, no. 1 (2024): e0295054. http://dx.doi.org/10.1371/journal.pone.0295054.

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Processing faces accurately and efficiently is a key capability of humans and other animals that engage in sophisticated social tasks. Recent studies reported a decoupled coding for faces in the primate inferotemporal cortex, with two separate neural populations coding for the geometric position of (texture-free) facial landmarks and for the image texture at fixed landmark positions, respectively. Here, we formally assess the efficiency of this decoupled coding by appealing to the information-theoretic notion of description length, which quantifies the amount of information that is saved when

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Li, Feng. "Simulation of Video Image Fault Tolerant Coding Transmission in Digital Multimedia." Mathematical Problems in Engineering 2022 (September 13, 2022): 1–7. http://dx.doi.org/10.1155/2022/4657091.

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In order to effectively improve the quality of video image transmission, this paper proposes a method of digital multimedia video image coding. The transmission of digital multimedia video image fault-tolerant coding requires sparse decomposition of a digital multimedia video image to obtain the linear form of the image and complete the transmission of video image fault-tolerant coding. The traditional method of fault-tolerant coding is based on human visual characteristics but ignores the linear form of the digital multimedia video image, which leads to the unsatisfactory effect of coding and

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Aborass, Maisam A. "Implementation of Adaptive Coding Approach Based on Periodic Walsh Piecewise-Linear Transform for Digital Grayscale Image." International Science and Technology Journal 34, no. 2 (2024): 1–12. http://dx.doi.org/10.62341/maai2987.

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An adaptive coding technique of gray level images using Periodic Walsh Piecewise-Linear (PWL) transform will be presented in this paper. In this technique the image sub-blocks are divided into four classes according to the level of image activity. The ac energy is used as a measure of the sub-block activity. Adaptivity is obtained by distributing bits among glasses. More bits are assigned to classes of higher activity and fewer bits to lower activity classes. The coding method employs an integer bit allocation scheme and Llyod-Max quantizers. Comparison of coding real images using the adaptive

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KAMAL, A. R. NADIRA BANU, S. THAMARAI SELVI, and HENRY SELVARAJ. "ITERATION-FREE FRACTAL CODING FOR IMAGE COMPRESSION USING GENETIC ALGORITHM." International Journal of Computational Intelligence and Applications 07, no. 04 (2008): 429–46. http://dx.doi.org/10.1142/s1469026808002399.

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An iteration-free fractal coding for image compression is proposed using genetic algorithm (GA) with elitist model. The proposed methodology reduces the coding process time by minimizing intensive computations. The proposed technique utilizes the GA, which greatly decreases the search space for finding the self-similarities in the given image. The performance of the proposed method is compared with the iteration-free fractal-based image coding using vector quantization method for both single block and Quad tree partition on benchmark images for parameters such as image quality and coding time.

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Pankiraj, Jeya Bright, Vishnuvarthanan Govindaraj, Yudong Zhang, Pallikonda Rajasekaran Murugan, and Anisha Milton. "Development of Scalable Coding of Encrypted Images Using Enhanced Block Truncation Code." Webology 19, no. 1 (2022): 1620–39. http://dx.doi.org/10.14704/web/v19i1/web19109.

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Only few researchers are reported on scalable coding of encrypted images, and it is an important area of research. In this paper, a novel method of scalable coding of encrypted images using Enhanced Block Truncation Code (EBTC) has been proposed. The raw image is compressed using EBTC and then encrypted using the pseudo-random number (PSRN) at the transmitter and the Key is disseminated to the receiver. The transmitted image is decrypted at the receiver by using the PSRN key. Finally, the output image is constructed using EBTC, scaled by scaling factor 2 and Bilinear Interpolation Technique. T

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HU, XIYUAN, SILONG PENG, and WEN-LIANG HWANG. "MULTIPLE COMPONENT PREDICTIVE CODING OF IMAGES." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 02 (2013): 1350012. http://dx.doi.org/10.1142/s0219691313500124.

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The conventional multiple component image compression approach separates the input image into several components, each of which is predicted and encoded independently. This approach creates redundancy because the prediction methods as well as the residual subcomponents must be transmitted. In this paper, we propose a new multiple-component predictive coding framework. First, we separate the reconstructed image into several subcomponents. Then, we use the previously encoded subcomponent to predict the current block, and then combine the prediction residuals of each subcomponent. To separate an

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Sadeeq, Haval Tariq, Thamer Hassan Hameed, Abdo Sulaiman Abdi, and Ayman Nashwan Abdulfatah. "Image Compression Using Neural Networks: A Review." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 14 (2021): 135–53. http://dx.doi.org/10.3991/ijoe.v17i14.26059.

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Computer images consist of huge data and thus require more memory space. The compressed image requires less memory space and less transmission time. Imaging and video coding technology in recent years has evolved steadily. However, the image data growth rate is far above the compression ratio growth, Considering image and video acquisition system popularization. It is generally accepted, in particular that further improvement of coding efficiency within the conventional hybrid coding system is increasingly challenged. A new and exciting image compression solution is also offered by the deep co

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Reid, M. M., R. J. Millar, and N. D. Black. "Second-generation image coding." ACM Computing Surveys 29, no. 1 (1997): 3–29. http://dx.doi.org/10.1145/248621.248622.

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22

Nohre, R. "Fragmentation-based image coding." Electronics Letters 31, no. 11 (1995): 870–71. http://dx.doi.org/10.1049/el:19950583.

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Chen, D., and A. C. Bovik. "Visual pattern image coding." IEEE Transactions on Communications 38, no. 12 (1990): 2137–46. http://dx.doi.org/10.1109/26.64656.

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Pardàs, Montse. "Object-based image coding." Vistas in Astronomy 41, no. 3 (1997): 455–61. http://dx.doi.org/10.1016/s0083-6656(97)00051-2.

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Zhou Wang and A. C. Bovik. "Embedded foveation image coding." IEEE Transactions on Image Processing 10, no. 10 (2001): 1397–410. http://dx.doi.org/10.1109/83.951527.

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Tsai, M. J., J. D. Villasenor, and F. Chen. "Stack-run image coding." IEEE Transactions on Circuits and Systems for Video Technology 6, no. 5 (1996): 519–21. http://dx.doi.org/10.1109/76.538934.

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Silva, V., L. Cruz, F. Lopes, A. Rodrigues, and L. de Sá. "Multiprocessor based image coding." Microprocessing and Microprogramming 32, no. 1-5 (1991): 343–48. http://dx.doi.org/10.1016/0165-6074(91)90368-4.

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Qiu, G., and G. D. Finlayson. "Image Coding for Classification." Color and Imaging Conference 7, no. 1 (1999): 278–82. http://dx.doi.org/10.2352/cic.1999.7.1.art00053.

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Fowler, J. E., M. R. Carbonara, and S. C. Ahalt. "Image coding using differential vector quantization image coding using differential vector quantization." IEEE Transactions on Circuits and Systems for Video Technology 3, no. 5 (1993): 350–67. http://dx.doi.org/10.1109/76.246087.

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Hu, Yu-Chen, Min-Hui Lin, and Ji-Han Jiang. "A Novel Color Image Hiding Scheme Using Block Truncation Coding." Fundamenta Informaticae 70, no. 4 (2006): 317–31. https://doi.org/10.3233/fun-2006-70402.

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In this paper, a novel color image hiding scheme that is capable of hiding two color secret images into a color host image is proposed. The secret images to be embedded are first compressed by the single bit map block truncation coding. DES encryption is then conducted on the compressed message before the secret image is embedded into the rightmost 3, 2, 3 bits of the R, G, B channels of every pixel in the host image. The experimental results show that our scheme provides an average secret image quality of 29.220 dB. In addition to the improved quality of both host images and retrieved secret

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Prof. Sathish. "Light Field Image Coding with Image Prediction in Redundancy." Journal of Soft Computing Paradigm 2, no. 3 (2020): 160–67. http://dx.doi.org/10.36548/jscp.2020.3.003.

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The proposed work involves a hybrid data representation using efficient light field coding. The existing light field coding solution are implemented using sub-aperture or micro-images. However, the full capacity in terms of intrinsic redundancy in light field images is not completely explored. This paper represents a hybrid data representation which explores four major redundancy types. Using coding block, the most predominant redundancy is exploited to find the optimum coding solution that provides maximum flexibility. To show how efficient the hybrid representation works, we have proposed a

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Tarchouli, Marwa, Marc Riviere, Thomas Guionnet, Wassim Hamidouche, Meriem Outtas, and Olivier Deforges. "Patch-Based Image Learned Codec using Overlapping." Signal & Image Processing : An International Journal 14, no. 1 (2023): 1–21. http://dx.doi.org/10.5121/sipij.2023.14101.

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End-to-end learned image and video codecs, based on auto-encoder architecture, adapt naturally to image resolution, thanks to their convolutional aspect. However, while coding high resolution images, these codecs face hardware problems such as memory saturation. This paper proposes a patch-based image coding solution based on an end-to-end learned model, which aims to remedy to the hardware limitation while maintaining the same quality as full resolution image coding. Our method consists in coding overlapping patches of the image and reconstructing them into a decoded image using a weighting f

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Grimes, David B., and Rajesh P. N. Rao. "Bilinear Sparse Coding for Invariant Vision." Neural Computation 17, no. 1 (2005): 47–73. http://dx.doi.org/10.1162/0899766052530893.

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Recent algorithms for sparse coding and independent component analysis (ICA) have demonstrated how localized features can be learned from natural images. However, these approaches do not take image transformations into account. We describe an unsupervised algorithm for learning both localized features and their transformations directly from images using a sparse bilinear generative model. We show that from an arbitrary set of natural images, the algorithm produces oriented basis filters that can simultaneously represent features in an image and their transformations. The learned generative mod

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Liu, Fu, Wen Wei Fu, and Hui Tang. "Encoding and Reconstruction about Video Image via Compressed Sensing." Advanced Materials Research 765-767 (September 2013): 2617–20. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2617.

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A new method for encoding and reconstruction high quality video image is given in this paper which uses the theory of compressed sensing. First the image frame of video is transformed into DCT domain. Then Image coding and decoding process using CS theory is given, frame I in image sequences is coded by frame coding mode after doing CS sampling to the DCT coefficients and the difference vector dv of the t-th fame for fame P. CS reconstruction and IDCT are done during decoding. Finally, the high quality reconstructed image is obtained. The experimental results shows that for images with sparsen

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Frajka, Tama´s. "Residual image coding for stereo image compression." Optical Engineering 42, no. 1 (2003): 182. http://dx.doi.org/10.1117/1.1526492.

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Er., Neha Saini Mr. Naveen Dhillion Mr. Manit Kapoor. "A PAPER ON A COMPARATIVE STUDY BLOCK TRUNCATING CODING, WAVELET, FRACTAL IMAGE COMPRESSION & EMBEDDED ZERO TREE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 7 (2016): 1052–61. https://doi.org/10.5281/zenodo.57987.

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Many different image compression techniques currently exist for the compression of different types of images. Image compression is fundamental to the efficient and cost-effective use of digital imaging technology and applications. In this study Image compression was applied to compress and decompress image at various compression ratios. Compressing an image is significantly different than compressing raw binary data. For this different compression algorithm are used to compress images. Fractal image compression has been widely used to compress the image.  We undertake a study of the perfo

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Syuhada, Ibnu. "Implementasi Algoritma Arithmetic Coding dan Sannon-Fano Pada Kompresi Citra PNG." TIN: Terapan Informatika Nusantara 2, no. 9 (2022): 527–32. http://dx.doi.org/10.47065/tin.v2i9.1027.

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The rapid development of technology plays an important role in the rapid exchange of information. In sending information in the form of images, there are still problems, including because of the large size of the image so that the solution to this problem is to perform compression. In this thesis, we will implement and compare the performance of the Arithmetic Coding and Shannon-Fano algorithms by calculating the compression ratio, compressed file size, compression and decompression process speed. Based on all test results, that the Arithmetic Coding algorithm produces an average compression r

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Götting, Detlef, Achim Ibenthal, and Rolf-Rainer Grigat. "Fractal Image Coding and Magnification Using Invariant Features." Fractals 05, supp01 (1997): 65–74. http://dx.doi.org/10.1142/s0218348x97000644.

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Fractal image coding has significant potential for the compression of still and moving images and also for scaling up images. The objective of our investigations was twofold. First, compression ratios of factor 60 and more for still images have been achieved, yielding a better quality of the decoded picture material than standard methods like JPEG. Second, image enlargement up to factors of 16 per dimension has been realized by means of fractal zoom, leading to natural and sharp representation of the scaled image content. Quality improvements were achieved due to the introduction of an extende

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Oğraş, Hidayet. "A New Data Coding Algorithm for Secure Communication of Image." Chaos Theory and Applications 6, no. 4 (2024): 284–93. https://doi.org/10.51537/chaos.1517688.

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This paper proposes a new entropy-sensitive based data coding algorithm for the secure communication of image information between transceiver systems. The proposed algorithm utilizes chaos theory and the image information content of the reference image to create sensitivity on the decoding side for a high level of secrecy. It successfully recovers secret images at the receiver’s side using secret code series derived from both the secret and reference images, instead of direct transmission of secret image. The image information can be retrieved only through the same reference image, the same sy

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Mahajan, Vipul R., and Alka Khade. "A Survey: Content Based Image Retrieval using Block Truncation Coding." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 12 (2018): 46. http://dx.doi.org/10.23956/ijarcsse.v7i12.495.

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A new approach to index color images using the features extracted from the error diffusion Block truncation coding (EDBTC). The EDBTC produces two color quantizes and a bitmap Image, which is further, managed using vector quantization (VQ) to create the image feature Descriptor. Herein two features are presented namely, colour histogram feature (CHF),bit Pattern histogram feature (BHF) to measure the similarity between a query image and the Target image in database. The CHF and BHF are calculated from the VQ-indexed color quantized and VQ- indexed bitmap image, respectively. The distance calcu

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Et. al., S. Anitha,. "Image Compression based on Octagon Based Intra Prediction." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 10 (2021): 6144–51. http://dx.doi.org/10.17762/turcomat.v12i10.5452.

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Recently image coding has been an important research area in many fields. Various compression algorithms have been developed in different ways for image compression. One of the ways in image coding is prediction based image coding. This paper proposes a novel technique for finding the prediction of a current pixel. Instead of traditional four mode prediction, this paper proposes an eight mode prediction scheme. The proposed method is tested with nine traditional images and compared with four recent methods. Experimental results substantially proved that the proposed method is better than recen

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Wu, Hao. "Image Self-Coding Algorithm Based on IoT Perception Layer." Mobile Information Systems 2022 (August 3, 2022): 1–11. http://dx.doi.org/10.1155/2022/9910655.

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In fact, with the quick growth of IoT-related industries in recent years, multimedia contents such as digital image videos have also shown explosive growth. In the sensing layer of the three-layer IoT architecture, sensors are the most critical part, which mainly sense the state of the environment. In this paper, an image self-coding algorithm based on the IoT perception layer is proposed. There is no specific encoding algorithm for the pictures collected by the current Internet of Things network perception layer. This results in poor search results for the network images collected by the sens

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LU, JIAN, JIAPENG TIAN, CHEN XU, and YURU ZOU. "A DICTIONARY LEARNING APPROACH FOR FRACTAL IMAGE CODING." Fractals 27, no. 02 (2019): 1950020. http://dx.doi.org/10.1142/s0218348x19500208.

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In recent years, sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, this paper investigates incorporating a dictionary learning approach into fractal image coding, which leads to a new model containing three terms: a patch-based sparse representation prior over a learned dictionary, a quadratic term measuring the closeness of the underlying image to a fractal image, and a data-fidelity term capturing the statistics of Gaussian noise. After the dictionary is learned, the resulting optimization problem with fractal coding can be solved

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Khaitu, Shree Ram, and Sanjeeb Prasad Panday. "Fractal Image Compression Using Canonical Huffman Coding." Journal of the Institute of Engineering 15, no. 1 (2020): 91–105. http://dx.doi.org/10.3126/jie.v15i1.27718.

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Image Compression techniques have become a very important subject with the rapid growth of multimedia application. The main motivations behind the image compression are for the efficient and lossless transmission as well as for storage of digital data. Image Compression techniques are of two types; Lossless and Lossy compression techniques. Lossy compression techniques are applied for the natural images as minor loss of the data are acceptable. Entropy encoding is the lossless compression scheme that is independent with particular features of the media as it has its own unique codes and symbol

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LIAN, SHIGUO, XI CHEN, and DENGPAN YE. "SECURE FRACTAL IMAGE CODING BASED ON FRACTAL PARAMETER ENCRYPTION." Fractals 17, no. 02 (2009): 149–60. http://dx.doi.org/10.1142/s0218348x09004405.

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In recent work, various fractal image coding methods are reported, which adopt the self-similarity of images to compress the size of images. However, till now, no solutions for the security of fractal encoded images have been provided. In this paper, a secure fractal image coding scheme is proposed and evaluated, which encrypts some of the fractal parameters during fractal encoding, and thus, produces the encrypted and encoded image. The encrypted image can only be recovered by the correct key. To maintain security and efficiency, only the suitable parameters are selected and encrypted through

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Abdelwahab, Ahmed A. "Inter-Image Similarity-Based Fast Adaptive Block Size Vector Quantizer for Image Coding." International Journal of Image and Graphics 17, no. 03 (2017): 1750017. http://dx.doi.org/10.1142/s0219467817500176.

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Block coding is well known in the digital image coding literature. Vector quantization and transform coding are examples of well-known block coding techniques. Different images have many similar spatial blocks introducing inter-image similarity. The smaller the block size, the higher the inter-image similarity. In this paper, a new block coding algorithm based on inter-image similarity is proposed where it is claimed that any original image can be reconstructed from the blocks of any other image. The proposed algorithm is simply a vector quantization without the need to a codebook design algor

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Wang, Yuer, Zhong Jie Zhu, and Wei Dong Chen. "HVS-Based Low Bit-Rate Image Compression." Applied Mechanics and Materials 511-512 (February 2014): 441–46. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.441.

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Image coding and compression is one of the most key techniques in the area of image signal processing, However, most of the existing coding methods such as JPEG, employ the similar hybrid architecture to compress images and videos. After many years of development, it is difficult to further improve the coding performance. In addition, most of the existing image compression algorithms are designed to minimize difference between the original and decompressed images based on pixel wise distortion metrics, such as MSE, PSNR which do not consider the HVS features and is not able to guarantee good p

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Hu, Yu-Chen, Chun-Chi Lo, Wu-Lin Chen, and Chia-Hsien Wen. "Joint image coding and image authentication based on absolute moment block truncation coding." Journal of Electronic Imaging 22, no. 1 (2013): 013012. http://dx.doi.org/10.1117/1.jei.22.1.013012.

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Hashimoto, Hideo. "Introduction of image data compression. (5). Image data coding algorithm. II. Transform coding." Journal of the Institute of Television Engineers of Japan 43, no. 10 (1989): 1145–52. http://dx.doi.org/10.3169/itej1978.43.1145.

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Hatori, Yoshinori. "Introduction to image data compression. (4). Image data coding algorithm. I. Predictive coding." Journal of the Institute of Television Engineers of Japan 43, no. 9 (1989): 949–56. http://dx.doi.org/10.3169/itej1978.43.949.

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