A Comparative Analysis of Image Coding Methods A State-of-the-Art Survey
DOI:
https://doi.org/10.12723/mjs/sp2.2Keywords:
Compression, Digital Image, Network Capacity, Internet Storage, Redundancy, Human Visual SystemAbstract
Abstract: As a result of new advanced technology and increased capacity of existing ones, bandwidth requirement is increasing exponentially.Many of the current initiatives in the field of data compression are described by it. The objective of these endeavors is to propose new methods for encoding information sources like audio, images, and video in a manner that reduces the number of bits needed to represent the source content without noticeably compromising the quality. There is a necessity of the new methods that works by reducing the source data without significantly limiting the quality . This is the main intension of these works. In the recent, there has been a significant increase in image compression research, which corresponds with a noteworthy rise in the generation of digital data in the form of images. The objective is to preserve the vital information contained in an image while representing in the fewest possible bits.
References
Bovik, A.C. Handbook of Image and Video Processing; Academic Press: Cambridge, MA, USA, 2010.
Sayood, K. Introduction to Data Compression; Morgan Kaufmann: Burlington, MA, USA, 2017.
Salomon, D.; Motta, G. Handbook of Data Compression; Springer Science & Business Media: Berlin, Germnay, 2010.
Hui Zha, Progressive Lossless Image Compression Using Image Decomposition and Context Quantization, 2008
Kitty Arora, Manish Shukla, A Comprehensive Review of Image Compression Techniques, 2014 .
Rahman, M.A.; Hamada, M. Lossless Image Compression Techniques: A State-of-the-Art Survey. Symmetry 2019, 11,
https://doi.org/10.3390/sym11101274
Uthayakumar Jayasankar, Vengattaraman Thirumal, Dhavachelvan Ponnurangam, A survey on data compression
techniques: From the perspective of data quality, coding schemes, data type and applications, Journal of King Saud
University - Computer and Information Sciences, Volume 33, Issue 2, 2021,Pages 119-140,ISSN 1319-1578, https://
doi.org/10.1016/j.jksuci.2018.05.006.
Haque,M.R.; Ahmed, F.Image Data Compression with JPEG and JPEG2000. Avaliableonline.http://eeweb.poly.edu/~yao/EE3414_S03/Projects/Loginova_Zhan_ ImageCompressing_Rep.pdf(accessed on 1 October 2019).
Poonlap Lamsrichan, “Straightforward Color Image Compression Using True-Mean Multi-Level Block Truncation Coding”, IEEE International Conference on Consumer Electronics (ICCE), IEEE 2021.
Fabian Mentzer George Toderici Michael Tschannen and Eirikur Agustsson. High-fidelity generative image
compression. arXiv preprint arXiv:2006.09965 2020.
Lu, G., Ge, X., Zhong, T., Geng, J. and Hu, Q., 2022. Preprocessing Enhanced Image Compression for Machine
Vision. arXiv preprint arXiv:2206.05650.
Zhang, F., Xu, Z., Chen, W., Zhang, Z., Zhong, H., Luan, J. and Li, C., 2019. An image compression method for
video surveillance system in underground mines based on residual networks and discrete wavelet transform.
Electronics, 8(12), p.1559.
H. H. Cheng C. A. Chen L. J. Lee T. L. Lin Y. S. Chiou and S. L. Chen “A low-complexity color image compression
algorithm based on AMBTC” 2019 IEEE International Conference on Consumer ElectronicsTaiwan (ICCE-TW)
May 2019.
Latha, Heggere & Ramaprasath, Alagarswamy. (2023). HWCD: A hybrid approach for image compression using
wavelet, encryption using confusion, and decryption using diffusion scheme. Journal of Intelligent Systems. 32.
1515/jisys-2022-9056.
Emiel Hoogeboom Jorn WT Peters Rianne Van Den Berg and Max Welling. Integer discrete flows and lossless
compression. arXiv preprint arXiv:1905.07376 2019. 1.
UmaMaheswari, S. and SrinivasaRaghavan, V., 2021. Lossless medical image compression algorithm using
tetrolet transformation. Journal of Ambient Intelligence and Humanized Computing, 12(3), pp.4127-4135.
C. A. Chen S. L. Chen C. H. Lioa and P. A. R. Abu “Lossless CFA image compression chip design for wireless capsule
endoscopy” IEEE Access vol. 7 pp. 107047-107057 Jul. 2019.
Jooyoung Lee Seunghyun Cho and Seung-Kwon Beack. Context-adaptive entropy model for end-to-end optimized
image compression. arXiv preprint arXiv:1809.10452 2018.1.
Mishra, D., Singh, S.K. and Singh, R.K., 2022. Deep architectures for image compression: a critical review. Signal Processing, 191, p.108346.
Mu Li WangmengZuoShuhangGuDebin Zhao and David Zhang. Learning convolutional networks for contentweighted image compression. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pages 3214–3223 2018.
Kaur, Rajandeep, and Pooja Choudhary. “A review of image compression techniques.” Int. J. Comput. Appl 142.1
(2016): 8-11.
Elakkiya, S., and K. S. Thivya. “Comprehensive review on lossy and lossless compression techniques.” Journal of
The Institution of Engineers (India): Series B (2021): 1-10.
Dhawan, Sachin. “A review of image compression and comparison of its algorithms.” International Journal of
electronics & Communication technology 2.1 (2011): 22-26.
Zhou, Lei, et al. “End-to-end Optimized Image Compression with Attention Mechanism.” CVPR workshops. 2019.
Bindu, Kiran, Anita Ganpati, and Aman Kumar Sharma. “A comparative study of image compression
algorithms.” International Journal of Research in Computer Science 2.5 (2012): 37.
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