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Lossless and near lossless compression of images with sparse histograms

Lossless and near lossless compression of images with sparse histograms Histogram sparseness is an unexpected characteristic by most of the lossless compression algorithms that have been designed mainly to process continuous-tone images. The compression efficiency of most of lossless image encoders is severely affected when handling sparse histogram images. In this paper, we presented an analysis of the histogram sparseness impact on lossless image compression standards and a new preprocessing technique was proposed in order to improve the compression performance for sparse histogram images. The proposed technique takes advantage of the high likelihood between neighboring image blocks. For each image block, the proposed method associates the most reduced set representing its active symbols and makes the histogram dense. This technique proved to be efficient without applying any modification to the basic code of the state-of the art lossless image compression techniques. We showed experimentally that the proposed method outperforms JPEG-LS, CALIC and JPEG 2000 and achieves lower bitrates. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Signal and Imaging Systems Engineering Inderscience Publishers

Lossless and near lossless compression of images with sparse histograms

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1748-0698
eISSN
1748-0701
DOI
10.1504/IJSISE.2020.113562
Publisher site
See Article on Publisher Site

Abstract

Histogram sparseness is an unexpected characteristic by most of the lossless compression algorithms that have been designed mainly to process continuous-tone images. The compression efficiency of most of lossless image encoders is severely affected when handling sparse histogram images. In this paper, we presented an analysis of the histogram sparseness impact on lossless image compression standards and a new preprocessing technique was proposed in order to improve the compression performance for sparse histogram images. The proposed technique takes advantage of the high likelihood between neighboring image blocks. For each image block, the proposed method associates the most reduced set representing its active symbols and makes the histogram dense. This technique proved to be efficient without applying any modification to the basic code of the state-of the art lossless image compression techniques. We showed experimentally that the proposed method outperforms JPEG-LS, CALIC and JPEG 2000 and achieves lower bitrates.

Journal

International Journal of Signal and Imaging Systems EngineeringInderscience Publishers

Published: Jan 1, 2020

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