Relevant bibliographies by topics / Image coding

Academic literature on the topic 'Image coding'

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Published: 4 June 2021
Last updated: 25 May 2024

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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 (October 19, 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 compression using neural network, fuzzy logic and neuro-fuzzy logic to preserve higher spectral representation to maintain finer edge information’s, and relational coding for inter band coefficients to achieve high compressions. The developed image coding model is evaluated over various quality factors. From the simulation results it is observed that the proposed image coding system can achieve efficient compression performance compared with existing block coding and JPEG coding approaches, even under resource constraint environments.
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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 (March 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 this research, convolutional neural networks are used as the predictor which estimates a pair of target pixels from the values of pixels which have already been decoded and adjacent to the target row. It helps to reduce the redundancy by sending the error between the real value and its predicted value. We also show its advantage by experimental results.
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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 (February 13, 2020): 359–68. http://dx.doi.org/10.5373/jardcs/v12sp1/20201082.

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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 (September 1, 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 algorithm that produces images that can be decoded by standard JPEG decoders, which are commonly used worldwide. The proposed method consists of three procedures: block classification, glossiness enhancement, and non-glossiness information reduction. In block classification, the types of glossiness in a target image are classified using block units. In glossiness enhancement, the glossiness in each type of block is emphasized to reduce the amount of degradation of glossiness during JPEG encoding. The third procedure, non-glossiness information reduction, further compresses the information while maintaining the glossiness by reducing the information in each block that does not represent the glossiness in the image. To test the effectiveness of the proposed method, the authors conducted a subjective evaluation experiment using paired comparison of images coded by the proposed method and JPEG images with the same data size. The glossiness was found to be better preserved in images coded by the proposed method than in the JPEG images.
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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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Kumar, Vikas. "Compression Techniques Vs Huffman Coding." International Journal of Informatics and Communication Technology (IJ-ICT) 4, no. 1 (April 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 technique is simple in implementation and utilizes less memory compression to other. A software algorithm has been developed and implemented to compress and decompress the given image using Huffman coding techniques in a MATLAB platform.</p><p> </p>
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Reid, M. M., R. J. Millar, and N. D. Black. "Second-generation image coding." ACM Computing Surveys 29, no. 1 (March 1997): 3–29. http://dx.doi.org/10.1145/248621.248622.

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Nohre, R. "Fragmentation-based image coding." Electronics Letters 31, no. 11 (May 25, 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 (January 1997): 455–61. http://dx.doi.org/10.1016/s0083-6656(97)00051-2.

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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
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-banda de mais baixa freqüência é codificada com DPCM, enquanto as demais SBB são codificadas por PCM. Como inovação, é proposto o uso do algoritmo de Lempel-Ziv na codificação sem perdas (compactação) das sub-bandas quantizadas. Na compactação são empregados os algoritmos de Huffman e LZW (modificação do LZA). Os resultados das simulações são apresentados em termos da taxa (bits/pixel) versus relação sinal ruído de pico e em termos de analise subjetiva das imagens reconstruídas. Os resultados obtidos indicam um desempenho de compressão superior quanto o algoritmo de Huffman é usado, comparado com o algoritmo LZW. A melhoria de desempenho, na técnica de decomposição em sub-bandas, observada com o algoritmo de Huffman foi superior (2dB acima). Todavia, tendo em vista as vantagens da universalidade do algoritmo de Lempel-Ziv, deve-se continuar a investigar o seu desempenho implementado de forma diferente do explorado neste trabalho.
This work focus on the problem of image compression, with exploring the techniques of subband coding. The basic structure, used in the sirst part of this tesis, encompass the uniform decomposition of the image into 16 subbands. This procedure aims at reproducing the reults of Woods [1]. The component of the 16 subbands are quatized and coded and bits are optimally allocated among the subbands to minimize the mean-squared error. The quantizers desingned match the Generelized Gaussian Distribuition, which model the subband components. In the coding process, the lowest subband is DPCM coded while the higher subbands are coded with PCM. As an innovation, it is proposed the use of the algorithm LZW for coding without error (compaction) the quantized subbands. In the compactation process, the Huffamn and LZW algorithms are used. The simulation results are presented in terms of rate (bits/pel) versus peak signal-to-noise and subjective quality. The performance of the subband decomposition tecnique obtained with the Huffamn´s algorithm is about 2dB better than that obtained with the LZW. The universality of the Lempel-Ziv algorithm is, however, an advantage that leads us to think that further investigation should still be pursued.
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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.
Includes bibliographical references (leaves 72-74).
by Ujjaval Yogesh.
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. Bellingham, Wash., USA: SPIE--International Society for Optical Engineering, 1986.

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Woods, John W., ed. Subband Image Coding. Boston, MA: 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. Boston: Kluwer Academic Publishers, 1991.

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Nancy, Hubing, ed. Speech and image coding. New York: IEEE, 1992.

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Low bitrate image sequence coding. Linköping: Dept. of Electrical Engineering, Linköping University, 1993.

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Transform coding of images. London: Academic, 1985.

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London), Colloquium on "Low Bit Rate Image Coding" (1990. Low bit rate image coding: Colloquium. London: 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. [Washington, DC]: National Aeronautics and Space Administration, 1992.

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

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

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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, 43–59. Berlin, Heidelberg: 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, 89–103. Cham: 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, 647–87. Singapore: 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, 213–50. Boston, MA: 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, 509–81. Berlin, Heidelberg: 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, 217–28. Berlin, Heidelberg: 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, 1–41. Boston, MA: 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, 43–100. Boston, MA: 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, 101–41. Boston, MA: 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, 143–92. Boston, MA: 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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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 function. This approach manages to be on par with the performance of full resolution image coding using an end-to-end learned model, and even slightly outperform it, while being adaptable to different memory size. It is also compatible with any learned codec based on a conv/deconvolutional autoencoderarchitecture without having to retrain the model.
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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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Simoncelli, Eero, Edward H. Adelson, and Rajesh Hingorani. "Efficient image coding with QMF pyramids." In OSA Annual Meeting. Washington, D.C.: 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 the sensitivity of the human visual system. The good spatial localization of the kernels avoids ringing artifacts. The transform achieves localization without blocks and thus completely avoids block artifacts. The result is highly efficient coding that gives visually pleasing images.
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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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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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J. AHMED, Zainab, and Loay E. GEORGE. "LIGHTWEIGHT IMAGE COMPRESSION USING POLYNOMIAL AND TRANSFORM CODING." In V. International Scientific Congress of Pure, Applied and Technological Sciences. Rimar Academy, 2022. http://dx.doi.org/10.47832/minarcongress5-16.

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This article implies a proposal for a lossy compression system using polynomial coding with color and gray images. The proposed system contains two main parts: coding and decoding. In the coding, each block of the image is represented as a twodimensional quadratic polynomial to prune the main low variation component to get better compression gain. The size of each image block is variable depending on quadtree partitioning. Then, the signal residue of the polynomial representation (i.e., the residue is the component produced due to subtraction of polynomial from the original image); then the polynomial coefficients and the residue are encoded, separately in different manners. The transformation coding (i.e., Discrete Cosine transform or bi-Orthogonal Wavelet transform) is used to compress the residual component of each band. The applied transform on the residue band can map the values more compactly. Also, uniform quantization is applied to the residue to effectively reduce the number of consumed bits required to represent the residue. Various encoding operations are performed to encode the polynomial coefficients. Finally, the all-out output is compressed using an LZW encoder. The results of the conducted test indicated that the introduced structure is lightweight and its implementation is straightforward. Besides that, it can lead to high compression ratios with an acceptable fidelity tolerance. The results are comparable to those of the current study and it has made progress.
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Wang, Xiao, Ding Ding, Wei Jiang, Wei Wang, Xiaozhong Xu, Shan Liu, Brian Kulis, and Peter Chin. "Substitutional Neural Image Compression." In 2022 Picture Coding Symposium (PCS). IEEE, 2022. http://dx.doi.org/10.1109/pcs56426.2022.10018005.

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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. Fort Belvoir, VA: Defense Technical Information Center, March 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. Fort Belvoir, VA: Defense Technical Information Center, October 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), February 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, January 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. Fort Belvoir, VA: Defense Technical Information Center, May 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. Fort Belvoir, VA: Defense Technical Information Center, September 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. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada206548.

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DiGrande, Laura, Sue Pedrazzani, Elizabeth Kinyara, Melanie Hymes, Shawn Karns, Donna Rhodes, and Alanna Moshfegh. Field Interviewer– Administered Dietary Recalls in Participants’ Homes: A Feasibility Study Using the US Department of Agriculture’s Automated Multiple-Pass Method. RTI Press, May 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. Participants: English-speaking adults. Pregnant women and individuals who were fasting were excluded. Results: Among 133 interviews, most took place in living rooms (52%) or kitchens (22%). Mean interview time was 40 minutes (range 13–90), with no difference by interviewer type or portion estimator, although timing for nutritionist-led interviews declined significantly over the study period. Forty-five percent of participants referenced items from their homes to facilitate recall and portion estimation. Data entry and post-interview coding was evaluated and determined to be consistent with requirements for the National Health and Nutrition Examination Survey. Values for the number of food items consumed, food groups, energy intake (average of 3,011 kcal for men and 2,105 kcal for women), and key nutrients were determined to be plausible and within reasonably expected ranges regardless of interviewer type or portion estimator used. Conclusions: AMPM dietary recall interviews conducted in the home are feasible and may be preferable to clinical administration because of comfort and the opportunity for participants to access home items for recall. AMPMs administered by field interviewers using the food model booklet produced credible nutrition data that was comparable to AMPMs administered by nutritionists. Training field interviewers in dietary recall and conducting home interviews may be sensible choices for nutrition studies when response rates and cost are concerns.
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