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.
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