Article

Received 03.02.2025

Revised 10.12.2025

Accepted 20.06.2025

Retrieved from Vol. 28, No. 1, 2025

Pages 132 -144

Karnatov, S., & Gertsiy, O. (2025). Comparative analysis of the quality of fractal image compression with JPEG and JPEG2000 standards. The National Transport University Bulletin: A Scientific and Technical Journal, 28(1), 132-144. https://doi.org/10.32703/2617-9040-2025-45-10

Comparative analysis of the quality of fractal image compression with JPEG and JPEG2000 standards

Serhii Karnatov Oleksandr Gertsiy

This paper presents a comparative analysis of three image compression methods: JPEG, JPEG2000, and fractal compression (FIC). The theoretical foundations of each method are reviewed, including the discrete cosine transform (DCT) for JPEG, the discrete wavelet transform (DWT) for JPEG2000, and the iterated function system (IFS) for FIC. The performance of the algorithms is evaluated using a set of  metrics:  compression  ratio  (CR),  peak  signal-to-noise  ratio  (PSNR),  structural  similarity  index (SSIM), and the learned fragment image similarity metric (LPIPS). The analysis shows that JPEG2000 generally provides better quality at a given bitrate than JPEG, especially at high compression ratios, and  offers  additional  features  such  as  scalability,  but  this  advantage  is  rather  small.  JPEG  remains popular due to its simplicity and speed, but suffers from block artifacts. Fractal compression, despite its  theoretical  advantages,  such  as  potential  resolution  independence,  has  significant  drawbacks, including extremely slow encoding and often uncompetitive quality on general images. The application areas,  reasons  for  limited  implementation,  and  the  current  relevance  of  FIC  are  discussed.  It  is concluded that it is necessary to use various metrics for comprehensive quality assessment and that the choice of the optimal compression method depends on the specific requirements of the application

mage; compression lossy, lossless; fractal compression; LPIPS; PSNR; SSIM
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https://doi.org/10.32703/2617-9040-2025-45-10