DOI: 10.1515/cmam-2023-0097 ISSN: 1609-4840

Adaptive Image Compression via Optimal Mesh Refinement

Michael Feischl, Hubert Hackl
  • Applied Mathematics
  • Computational Mathematics
  • Numerical Analysis


The JPEG algorithm is a defacto standard for image compression. We investigate whether adaptive mesh refinement can be used to optimize the compression ratio and propose a new adaptive image compression algorithm. We prove that it produces a quasi-optimal subdivision grid for a given error norm with high probability. This subdivision can be stored with very little overhead and thus leads to an efficient compression algorithm. We demonstrate experimentally, that the new algorithm can achieve better compression ratios than standard JPEG compression with no visible loss of quality on many images. The mathematical core of this work shows that Binev’s optimal tree approximation algorithm is applicable to image compression with high probability, when we assume small additive Gaussian noise on the pixels of the image.

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