ANALYSIS OF THE LOSSY IMAGE COMPRESSION ALGORITHMS
DOI:
https://doi.org/10.31649/1999-9941-2023-58-3-59-64Keywords:
image compression, data compression, chroma subsampling, color quantization, discrete cosine transform, wavelet transform, fractal compressionAbstract
Abstract. The article discusses and conducts an analytical review of lossy image compression algorithms. Substantiated the relevance of the research with the help of statistical data. Considered and analyzed the color subsampling method. Reviewed, described, and analyzed the color quantization method, in particular, existing studies on the application of color quantization in combination with the discrete cosine transform. Highlighted the shortcomings of the existing research and formulated the possibility of further research using an expanded sample of images. Considered and analyzed in detail the compression based on the discrete cosine transform. Singled out the search for optimal quantization matrices as a promising direction of further research on improving the efficiency of the application of discrete cosine transformation. Highlighted the adaptive allocation of larger, multiples of the standard data blocks as a promising direction of research. Considered and analyzed the image compression method based on the wavelet transform. Formulated the direction of further research on the use of wavelets other than Cohen-Dobechy-Feuvo and LeGall-Tabatabay wavelet for image compression. Considered and analyzed the method of fractal compression. Formulated directions for further research, such as limiting the search depth and applying fractal compression in combination with discrete cosine transformation. Summarized directions for further research to improve the functional characteristics of the considered algorithms. The main scientific result of the conducted research is the selection of a list of promising research topics that will allow increasing the amount of data on methods, models and means of image compression. The practical value of the research is that it contains a list of research topics that can be used by researchers as material for further research.
References
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Van Den Branden Lambrecht, C. J. (2001). Vision Models and Applications to Image and Video Processing. Springer Science & Business Media.
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Leonardo C. Araujo, Joao P. H. Sansao, and Mario C. S. Junior. (2020). Effects of Color Quantization on JPEG Compression. International Journal of Image and Graphics. https://doi.org/10.1142/s0219467820500266
Qijun Wang, Ping Liu, Lei Zhang, Fan Cheng, Jianfeng Qiu, and Xingyi Zhang. (2022b). Rate-distortion optimal evolutionary algorithm for JPEG quantization with multiple rates. Knowledge Based Systems, 244, 108500. https://doi.org/10.1016/j.knosys.2022.108500
S. Naveen Kumar, M. V. Vamshi Bharadwaj and S. Subbarayappa, "Performance Comparison of Jpeg, Jpeg XT, Jpeg LS, Jpeg 2000, Jpeg XR, HEVC, EVC and VVC for Images," 2021 6th International Conference for Convergence in Technology (I2CT), Maharashtra, India, 2021, pp. 1-8, doi: 10.1109/I2CT51068.2021.9418160.
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D. Le Gall and A. Tabatabai, "Sub-band coding of digital images using symmetric short kernel filters and arithmetic coding techniques," ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing, New York, NY, USA, 1988, pp. 761-764 vol.2, doi: 10.1109/ICASSP.1988.196696.
Fresia, M., Natu, A., & Lavagetto, F. (2023b). Turbo Codes for the Transmission of JPEG2000 Compressed Imagery over Flat Rayleigh Fading Channels. ResearchGate. https://www.researchgate.net/publication/228943799_Turbo_Codes_for_the_Transmission_of_JPEG2000_Compressed_Imagery_over_Flat_Rayleigh_Fading_Channels
Wee Meng Woon, Anthony Tung Shuen Ho, Tao Yu, Siu Chung Tam, Siong Chai Tan and Lian Teck Yap, "Achieving high data compression of self-similar satellite images using fractal," IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120), Honolulu, HI, USA, 2000, pp. 609-611 vol.2, doi: 10.1109/IGARSS.2000.861646
Ali, A. H., Abbas, A. N., George, L. E., & Mokhtar, M. R. (2019). Image and audio fractal compression: Comprehensive review, enhancements and research directions. Indonesian Journal of Electrical Engineering and Computer Science, 15(3), 1564-1570.
References
Pantic, N. (2022, February 2). How Many Photos Will Be Taken in 2021? – Mylio Blog. Mylio Blog. https://blog.mylio.com/how-many-photos-will-be-taken-in-2021-stats
Data Storage Market Size, Share, Trends | Growth [2023-2030]. (n.d.). https://www.fortunebusinessinsights.com/data-storage-market-102991
N. Ahmed, T. Natarajan and K. R. Rao, “Discrete Cosine Transform” in IEEE Transactions on Computers, vol. C-23, no. 1, pp. 90-93, Jan. 1974, doi: 10.1109/T-C.1972.223784.
Van Den Branden Lambrecht, C. J. (2001). Vision Models and Applications to Image and Video Processing. Springer Science & Business Media.
P. Heckbert. “Color Image Quantization for Frame Buffer Display”, Computer Graphics, Vol 16, #3, pp. 297-303, 1982.
Leonardo C. Araujo, Joao P. H. Sansao, and Mario C. S. Junior. (2020). Effects of Color Quantization on JPEG Compression. International Journal of Image and Graphics. https://doi.org/10.1142/s0219467820500266
Qijun Wang, Ping Liu, Lei Zhang, Fan Cheng, Jianfeng Qiu, and Xingyi Zhang. (2022b). Rate-distortion optimal evolutionary algorithm for JPEG quantization with multiple rates. Knowledge Based Systems, 244, 108500. https://doi.org/10.1016/j.knosys.2022.108500
S. Naveen Kumar, M. V. Vamshi Bharadwaj and S. Subbarayappa, "Performance Comparison of Jpeg, Jpeg XT, Jpeg LS, Jpeg 2000, Jpeg XR, HEVC, EVC and VVC for Images," 2021 6th International Conference for Convergence in Technology (I2CT), Maharashtra, India, 2021, pp. 1-8, doi: 10.1109/I2CT51068.2021.9418160.
M. Unser and T. Blu, "Mathematical properties of the JPEG2000 wavelet filters," in IEEE Transactions on Image Processing, vol. 12, no. 9, pp. 1080-1090, Sept. 2003, doi: 10.1109/TIP.2003.812329.
D. Le Gall and A. Tabatabai, "Sub-band coding of digital images using symmetric short kernel filters and arithmetic coding techniques," ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing, New York, NY, USA, 1988, pp. 761-764 vol.2, doi: 10.1109/ICASSP.1988.196696.
Fresia, M., Natu, A., & Lavagetto, F. (2023b). Turbo Codes for the Transmission of JPEG2000 Compressed Imagery over Flat Rayleigh Fading Channels. ResearchGate. https://www.researchgate.net/publication/228943799_Turbo_Codes_for_the_Transmission_of_JPEG2000_Compressed_Imagery_over_Flat_Rayleigh_Fading_Channels
Wee Meng Woon, Anthony Tung Shuen Ho, Tao Yu, Siu Chung Tam, Siong Chai Tan and Lian Teck Yap, "Achieving high data compression of self-similar satellite images using fractal," IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120), Honolulu, HI, USA, 2000, pp. 609-611 vol.2, doi: 10.1109/IGARSS.2000.861646.
Ali, A. H., Abbas, A. N., George, L. E., & Mokhtar, M. R. (2019). Image and audio fractal compression: Comprehensive review, enhancements and research directions. Indonesian Journal of Electrical Engineering and Computer Science, 15(3), 1564-1570.
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