Relevant bibliographies by topics / Digital image compression

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Digital image compression'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 May 2022

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Journal articles on the topic "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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Dissertations / Theses on the topic "Digital image compression"

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Abdul-Amir, Said. "Digital image compression." Thesis, De Montfort University, 1985. http://hdl.handle.net/2086/10681.

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Due to the rapid growth in information handling and transmission, there is a serious demand for more efficient data compression schemes. compression schemes address themselves to speech, visual and alphanumeric coded data. This thesis is concerned with the compression of visual data given in the form of still or moving pictures. such data is highly correlated spatially and in the context domain. A detailed study of some existing data compression systems is presented, in particular, the performance of DPCM was analysed by computer simulation, and the results examined both subjectively and objectively. The adaptive form of the prediction encoder is discussed and two new algorithms proposed, which increase the definition of the compressed image and reduce the overall mean square error. Two novel systems are proposed for image compression. The first is a bit plane image coding system based on a hierarchic quadtree structure in a transmission domain, using the Hadamard transform as a kernel. Good compression has been achieved from this scheme, particularly for images with low detail. The second scheme uses a learning automata to predict the probability distribution of the grey levels of an image related to its spatial context and position. An optimal reward/punishment function is proposed such that the automata converges to its steady state within 4000 iterations • such a high speed of convergence together with Huffman coding results in efficient compression for images and is shown to be applicable to other types of data. . The performance and evaluation of all the proposed .'systems have been tested by computer simulation and the results presented both quantitatively and qualitatively."The advantages and disadvantages of each system are discussed and suggestions for improvement. given.
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Tokdemir, Serpil. "Digital compression on GPU." unrestricted, 2006. http://etd.gsu.edu/theses/available/etd-12012006-154433/.

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Thesis (M.S.)--Georgia State University, 2006.
Title from dissertation title page. Saeid Belkasim, committee chair; Ying Zhu, A.P. Preethy, committee members. Electronic text (90 p. : ill. (some col.)). Description based on contents viewed May 2, 2007. Includes bibliographical references (p. 78-81).
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Wyllie, Michael. "A comparative quantitative approach to digital image compression." Huntington, WV : [Marshall University Libraries], 2006. http://www.marshall.edu/etd/descript.asp?ref=719.

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Truong, Huy S. "Signal compression for digital television." Curtin University of Technology, School of Electrical and Computer Engineering, 1999. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=12068.

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Still image and image sequence compression plays an important role in the development of digital television. Although various still image and image sequence compression algorithms have already been developed, it is very difficult for them to achieve both compression performance and coding efficiency simultaneously due to the complexity of the compression process itself. As a results, improvements in the forms of hybrid coding, coding procedure refinement, new algorithms and even new coding concepts have been constantly tried, some offering very encouraging results.In this thesis, Block Adaptive Classified Vector Quantisation (BACVQ) has been developed as an alternative algorithm for still image compression. It is found that BACVQ achieves good compression performance and coding efficiency by combining variable block-size coding and classified VQ. Its performance is further enhanced by adopting both spatial and transform domain criteria for the image block segmentation and classification process. Alternative algorithms have also been developed to accelerate normal codebook searching operation and to determine the optimal sizes of classified VQ sub-codebooks.For image sequence compression, an adaptive spatial/temporal compression algorithm has been developed which divides an image sequence into smaller groups of pictures (GOP) using adaptive scene segmentation before BACVQ and variable block-size motion compensated predictive coding are applied to the intraframe and interframe coding processes. It is found the application of the proposed adaptive scene segmentation algorithm, an alternative motion estimation strategy and a new progressive motion estimation algorithm enables the performance and efficiency of the compression process to be improved even further.Apart from improving still image and image sequence compression algorithms, the application of parallel ++
processing to image sequence compression is also investigated. Parallel image compression offers a more effective approach than the sequential counterparts to accelerate the compression process and bring it closer to real-time operation. In this study, a small scale parallel digital signal processing platform has been constructed for supporting parallel image sequence compression operation. It consists of a 486DX33 IBM/PC serving as a master processor and two DSP (PC-32) cards as parallel processors. Because of the independent processing and spatial arrangement natures of most image processing operations, an effective parallel image sequence compression algorithm has been developed on the proposed parallel processing platform to significantly reduce the processing time of the proposed parallel image compression algorithms.
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Fu, Deng Yuan. "ADAPTIVE DIGITAL IMAGE DATA COMPRESSION BY RECURSIVE IDPCM." Thesis, The University of Arizona, 1985. http://hdl.handle.net/10150/275350.

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Ng, King-to, and 吳景濤. "Compression techniques for image-based representations." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B31244646.

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Khire, Sourabh Mohan. "Time-sensitive communication of digital images, with applications in telepathology." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29761.

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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Jayant, Nikil; Committee Member: Anderson, David; Committee Member: Lee, Chin-Hui. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Addlesee, Michael Dennis. "Aspects of image compression using the subband technique." Thesis, University of Cambridge, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385880.

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Sanikomm, Vikas Kumar Reddy. "Hardware Implementation of a Novel Image Compression Algorithm." ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/1032.

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Image-related communications are forming an increasingly large part of modern communications, bringing the need for efficient and effective compression. Image compression is important for effective storage and transmission of images. Many techniques have been developed in the past, including transform coding, vector quantization and neural networks. In this thesis, a novel adaptive compression technique is introduced based on adaptive rather than fixed transforms for image compression. The proposed technique is similar to Neural Network (NN)-based image compression and its superiority over other techniques is presented It is shown that the proposed algorithm results in higher image quality for a given compression ratio than existing Neural Network algorithms and that the training of this algorithm is significantly faster than the NN based algorithms. This is also compared to the JPEG in terms of Peak Signal to Noise Ratio (PSNR) for a given compression ratio and computational complexity. Advantages of this idea over JPEG are also presented in this thesis.
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Akhlaghian, Tab Fardin. "Multiresolution scalable image and video segmentation." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060227.100704/index.html.

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Books on the topic "Digital image compression"

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Kou, Weidong. Digital Image Compression. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-2361-8.

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Rabbani, Majid. Digital image compression techniques. Bellingham, Wash., USA: Spie Optical Engineering Press, 1991.

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Barnsley, Michael. Fractal image compression. Wellesley, Mass: AK Peters, 1993.

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Barnsley, Michael. Fractal image compression. Wellesley, Mass: AK Peters, 1993.

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Netravali, Arun N. Digital pictures: Representation and compression. New York: Plenum Press, 1988.

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Kou, Weidong. Digital image compression: Algorithms and standards. Boston: Kluwer Academic Publishers, 1995.

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Kou, Weidong. Digital Image Compression: Algorithms and Standards. Boston, MA: Springer US, 1995.

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Peter, Symes, ed. Digital video compression. New York: McGraw-Hill, 2004.

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G, Haskell Barry, ed. Digital pictures: Representation, compression, and standards. 2nd ed. New York: Plenum Press, 1995.

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Fractal and wavelet image compression techniques. Bellingham, Wash: SPIE Optical Engineering Press, 1999.

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Book chapters on the topic "Digital image compression"

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Sundararajan, D. "Image Compression." In Digital Image Processing, 363–405. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6113-4_13.

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Albert, Jürgen, and Jarkko Kari. "Digital Image Compression." In Monographs in Theoretical Computer Science, 453–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01492-5_11.

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Kou, Weidong. "Compression Algorithm Fundamentals." In Digital Image Compression, 9–36. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-2361-8_2.

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Kou, Weidong. "JBIG Compression Standard." In Digital Image Compression, 55–67. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-2361-8_4.

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Kou, Weidong. "JPEG Compression Standard." In Digital Image Compression, 69–101. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-2361-8_5.

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Kou, Weidong. "Digital Video Compression Standards." In Digital Image Compression, 103–29. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-2361-8_6.

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Kou, Weidong. "Principles of Digital Image Compression." In Digital Image Compression, 1–8. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-2361-8_1.

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Kou, Weidong. "CCITT Facsimile Compression Standards." In Digital Image Compression, 37–53. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-2361-8_3.

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Kou, Weidong. "Digital Image Compression Advanced Topics." In Digital Image Compression, 131–47. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-2361-8_7.

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Plataniotis, Konstantinos N., and Anastasios N. Venetsanopoulos. "Color Image Compression." In Digital Signal Processing, 279–328. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04186-4_7.

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Conference papers on the topic "Digital image compression"

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Saghri, John A., and Andrew G. Tescher. "Knowledge-Based Image Bandwidth Compression." In Applications if Digital Image Processing IX, edited by Andrew G. Tescher. SPIE, 1986. http://dx.doi.org/10.1117/12.976203.

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Willème, Alexandre, Benoît M. Macq, Antonin Descampe, Gaël Rouvroy, and Pascal Pellegrin. "JPEG XS-based frame buffer compression inside HEVC for power-aware video compression." In Applications of Digital Image Processing XL, edited by Andrew G. Tescher. SPIE, 2017. http://dx.doi.org/10.1117/12.2273170.

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Giuliani, Nicola, Biao Wang, Elena Alshina, and Laura Leal-Taixé. "Frame synthesis for video compression." In Applications of Digital Image Processing XLIV, edited by Andrew G. Tescher and Touradj Ebrahimi. SPIE, 2021. http://dx.doi.org/10.1117/12.2596884.

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Santoso, Alb Joko, F. Soesianto, and B. Yudi Dwiandiyanto. "Satellite image compression using wavelet." In Second International Conference on Digital Image Processing. SPIE, 2010. http://dx.doi.org/10.1117/12.855734.

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Testolina, Michela, Evgeniy Upenik, and Touradj Ebrahimi. "Comprehensive assessment of image compression algorithms." In Applications of Digital Image Processing XLIII, edited by Andrew G. Tescher and Touradj Ebrahimi. SPIE, 2020. http://dx.doi.org/10.1117/12.2569255.

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Dutta, Dipta Pratim, Samrat Deb Choudhury, Md Anwar Hussain, and Swanirbhar Majumder. "Digital Image Compression Using Neural Networks." In 2009 International Conference on Advances in Computing, Control, & Telecommunication Technologies (ACT 2009). IEEE, 2009. http://dx.doi.org/10.1109/act.2009.38.

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"A SURVEY ON DIGITAL IMAGE COMPRESSION." In Special Session on Internet of Things and Logistics Services. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003591605270534.

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Van Aerschot, Ward, Maarten Jansen, and Adhemar Bultheel. "Normal offsets for digital image compression." In Optics East 2005, edited by Frederic Truchetet and Olivier Laligant. SPIE, 2005. http://dx.doi.org/10.1117/12.629602.

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Topiwala, Pankaj, Madhu Krishnan, and Wei Dai. "Data Adaptive HDR Compression in VVC." In Applications of Digital Image Processing XLII, edited by Andrew G. Tescher and Touradj Ebrahimi. SPIE, 2019. http://dx.doi.org/10.1117/12.2530553.

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Topiwala, Pankaj, Madhu Krishnan, and Wei Dai. "HDR compression in the JVET codec." In Applications of Digital Image Processing XLI, edited by Andrew G. Tescher. SPIE, 2018. http://dx.doi.org/10.1117/12.2322028.

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Reports on the topic "Digital image compression"

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Wang, Jun, and H. K. Huang. Digital Mammographic Image Compression. Fort Belvoir, VA: Defense Technical Information Center, July 1995. http://dx.doi.org/10.21236/ada300271.

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Crooks, Marc W. Application of Image Compression to Digital Map Databases. Fort Belvoir, VA: Defense Technical Information Center, February 1993. http://dx.doi.org/10.21236/ada262808.

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