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Relevant bibliographies by topics / Image coding

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Academic literature on the topic 'Image coding'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Image coding"

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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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Dissertations / Theses on the topic "Image coding"

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Streit, Juergen Stefan. "Digital image coding." Thesis, University of Southampton, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361092.

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Chowdhury, Md Mahbubul Islam. "Image segmentation for coding." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0017/MQ55494.pdf.

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VASCONCELLOS, EDMAR DA COSTA. "SUB-BAND IMAGE CODING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1994. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8635@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Este trabalho aborda o problema da compressão de imagens explorando a técnica de codificação por sub-bandas(SBB). Como estrutura básica, usada na primeira parte do trabalho, tem-se a divisão da imagem em 16 sub-bandas buscando replicar os resultados de woods [1]. As componentes das 16 SBB são quantizadas e codificadas, e bits são alocados às SBB usando como critério a minimização do erro médio quadrático. Os quantizadores são projetados segundo uma distribuição Gaussiana Generalizada. Neste processo de codificação, a sub-ba

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Andersson, Tomas. "On error-robust source coding with image coding applications." Licentiate thesis, Stockholm : Department of Signals, Sensors and Systems, Royal Institute of Technology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4046.

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Bergström, Peter. "Eye-movement controlled image coding /." Linköping : Univ, 2003. http://www.bibl.liu.se/liupubl/disp/disp2003/tek831s.pdf.

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Silva, Eduardo Antonio Barros da. "Wavelet transforms for image coding." Thesis, University of Essex, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282495.

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Kubrick, Aharon H. "Image coding employing vector quantisation." Thesis, City University London, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.357009.

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Morgan, Pamela Sheila. "Medical image coding and segmentation :." Thesis, University of Bristol, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442206.

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Desai, Ujjaval Yogesh. "Coding of segmented image sequences." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/11984.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.<br>Includes bibliographical references (leaves 72-74).<br>by Ujjaval Yogesh.<br>M.Eng.

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Frajka, Tamás. "Image coding subject to constraints /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2003. http://wwwlib.umi.com/cr/ucsd/fullcit?p3090437.

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Books on the topic "Image coding"

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1945-, Kunt M., Huang Thomas S. 1936-, Society of Photo-optical Instrumentation Engineers., and Association nationale de la recherche technique., eds. Image coding. SPIE--International Society for Optical Engineering, 1986.

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Woods, John W., ed. Subband Image Coding. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4757-2119-5.

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1943-, Woods John W., ed. Subband image coding. Kluwer Academic Publishers, 1991.

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Nancy, Hubing, ed. Speech and image coding. IEEE, 1992.

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London), Colloquium on "Low Bit Rate Image Coding" (1990. Low bit rate image coding: Colloquium. Institution of Electrical Engineers, Electronics Division, 1990.

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C, Kwatra S., and United States. National Aeronautics and Space Administration., eds. Subband coding for image data archiving. National Aeronautics and Space Administration, 1992.

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Glover, D. Subband coding for image data archiving. National Aeronautics and Space Administration, 1992.

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Glover, Daniel. Subband coding for image data archiving. National Aeronautics and Space Administration, 1992.

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V, Dasarathy Belur, ed. Image data compression: Block truncation coding. IEEE Computer Society Press, 1995.

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C, Labit, ed. Motion analysis for image sequence coding. Elsevier, 1994.

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Book chapters on the topic "Image coding"

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Ma, Yide, Kun Zhan, and Zhaobin Wang. "Image Coding." In Applications of Pulse-Coupled Neural Networks. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_4.

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Xu, Xiang, Xingkun Wu, and Feng Lin. "Image Coding." In Cellular Image Classification. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47629-2_5.

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Zhang, Yu-Jin. "Image Coding." In Handbook of Image Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-5873-3_17.

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Girod, Bernd, Frank Hartung, and Uwe Horn. "Subband Image Coding." In The Kluwer International Series in Engineering and Computer Science. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0483-8_7.

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Ohm, Jens-Rainer. "Still Image Coding." In Multimedia Communication Technology. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18750-6_12.

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Wendler, Th. "Hierarchical Image Coding." In ASST ’87 6. Aachener Symposium für Signaltheorie. Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-73015-3_42.

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Pearlman, William A. "Performance Bounds for Subband Coding." In Subband Image Coding. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4757-2119-5_1.

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Vetterli, Martin. "Multirate Filter Banks for Subband Coding." In Subband Image Coding. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4757-2119-5_2.

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Smith, Mark J. T. "IIR Analysis/Synthesis Systems." In Subband Image Coding. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4757-2119-5_3.

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Simoncelli, Eero P., and Edward H. Adelson. "Subband Transforms." In Subband Image Coding. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4757-2119-5_4.

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Conference papers on the topic "Image coding"

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Krishnan, Madhu, Xin Zhao, and Shan Liu. "Advanced transform coding methods for next-generation video coding beyond AV1." In Applications of Digital Image Processing XLVII, edited by Andrew G. Tescher and Touradj Ebrahimi. SPIE, 2024. http://dx.doi.org/10.1117/12.3033114.

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Shi, Yunhui, Yanli Hou, Baocai Yin, and Wenpeng Ding. "Image coding approach based on image decomposition." In 2010 Picture Coding Symposium (PCS). IEEE, 2010. http://dx.doi.org/10.1109/pcs.2010.5702556.

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Tarchouli, Marwa, Sebastien Pelurson, Thomas Guionnet, Wassim Hamidouche, Meriem Outtas, and Olivier Deforges. "Patch-Based Image Coding with End-To-End Learned Codec using Overlapping." In 12th International Conference on Artificial Intelligence, Soft Computing and Applications. Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.122305.

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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 methodconsists in coding overlapping patches of the image and reconstruct them into a decoded image using a weighting funct

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Mobahi, Hossein, Shankar R. Rao, and Yi Ma. "Data-driven image completion by image patch subspaces." In 2009 Picture Coding Symposium (PCS). IEEE, 2009. http://dx.doi.org/10.1109/pcs.2009.5167452.

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Xiong, Ruiqin, Wenpeng Ding, Siwei Ma, and Wen Gao. "Improved autoregressive image model estimation for directional image interpolation." In 2010 Picture Coding Symposium (PCS). IEEE, 2010. http://dx.doi.org/10.1109/pcs.2010.5702531.

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Tiwari, Anil Kumar, and R. V. Raja Kumar. "A Switched Predictive Coding Method for Lossless Video Coding." In Image Processing (ICVGIP). IEEE, 2008. http://dx.doi.org/10.1109/icvgip.2008.105.

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Simoncelli, Eero, Edward H. Adelson, and Rajesh Hingorani. "Efficient image coding with QMF pyramids." In OSA Annual Meeting. Optica Publishing Group, 1987. http://dx.doi.org/10.1364/oam.1987.wl7.

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Quadrature mirror filters have been widely used in the subband coding of speech signals and have recently been applied to image coding as well.1 By using cascaded filtering and decimation we have derived QMF pyramids that are rapidly computed and afford very good data compression. High-pass kernels with even symmetry become possible when using a staggered sampling grid and can be combined separably for 2-D processing. Nonseparable 2-D kernels with interesting properties have also been derived. The good frequency localization permits data compression that takes advantage of scene statistics and

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Suzuki, Chihiro, Takamichi Miyata, and Yoshinori Sakai. "Image coding by using non-linear texture decomposition and image summarization." In 2010 Picture Coding Symposium (PCS). IEEE, 2010. http://dx.doi.org/10.1109/pcs.2010.5702561.

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Clarke, R. J. "Image coding and the coding standards." In 6th International Conference on Image Processing and its Applications. IEE, 1997. http://dx.doi.org/10.1049/cp:19970842.

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"Session: image coding." In 1988 IEEE International Symposium on Information Theory. IEEE, 1988. http://dx.doi.org/10.1109/isit.1988.22299.

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Reports on the topic "Image coding"

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Phoha, Shashi, and Mendel Schmiedekamp. Semantic Source Coding for Flexible Lossy Image Compression. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada464658.

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Klein, Stanley, and Amnon Silverstein. Spatio-Temporal Masking in Human Vision and Its Application to Image Coding. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada300556.

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Fu, Chi Yung ,. Petrich, L. I. ,. Lee, M. Image and video compression/decompression based on human visual perception system and transform coding. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/489146.

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Armstrong, Beth, Rebecca Gillespie, Maya King, and Abbie Collins. Exploring food behaviours in the UK student population: Interim findings. Food Standards Agency, 2022. http://dx.doi.org/10.46756/sci.fsa.lil128.

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Given the lack of evidence on this topic, co creation sessions were held with university students to identify key issues and inform the development of a nationally representative online survey. This report presents the main findings from the co-creation sessions and headline findings from the online survey. The full analysis, which will include results from image coding using the citizen science platform Zooniverse (Opens in a new window) (Opens in a new window), will be published at a later date.

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Orchard, Michael T. Wavelet Based Coding of Images and Video. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada394089.

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Marvel, Lisa M. Robust Source Coding of Images With Predictive Trellis - Coded Quantization. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada315312.

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NATIONAL COMMUNICATIONS SYSTEM WASHINGTON DC. Investigation of Vector Quantization for the Coding Gray Scale Images for Group 4 Facsimile. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada206548.

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DiGrande, Laura, Sue Pedrazzani, Elizabeth Kinyara, et al. Field Interviewer– Administered Dietary Recalls in Participants’ Homes: A Feasibility Study Using the US Department of Agriculture’s Automated Multiple-Pass Method. RTI Press, 2021. http://dx.doi.org/10.3768/rtipress.2021.mr.0045.2105.

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Objective: The purpose of this study was to assess the feasibility of administering the Automated Multiple-Pass Method (AMPM), a widely used tool for collecting 24-hour dietary recalls, in participants’ homes by field interviewers. Design: The design included computer-assisted personal interviews led by either a nutritionist (standard) or field interviewer. Portion estimators tested were a set of three-dimensional food models (standard), a two-dimensional food model booklet, or a tablet with digital images rendered via augmented reality. Setting: Residences in central North Carolina. Participa

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