Improved techniques for lossless image compression with reversible integer wavelet transforms

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Abstract
The past few years have seen an increasing interest in using reversible integer wavelets in image compression. Reversible integer wavelet image coders facilitate decompression from low bit rates all the way up to lossless reconstruction. However, in the past, specific implementations of such techniques, like S+P, could not match the lossless compression performance of state-of-the-art predictive coding techniques like CALIC. We demonstrate for the first time that reversible integer wavelets together with proper context modeling can match the lossless compression performance of CALIC. This can be done without increase in the essential complexity over S+P. Our findings present a strong argument for using subband coding as a unified, elegant approach for both lossy and lossless image compression. Specifically, in this paper we outline how to obtain significantly higher coding efficiency over S+P by utilizing better filters and better context modeling and entropy coding of wavelet coefficients.

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