Title: Lossless and near lossless compression of images with sparse histograms

Authors: Souha Jallouli; Sonia Zouari; Nouri Masmoudi; Atef Masmoudi

Addresses: Laboratory of Electronics and Technologie of Information, National Engineering School of Sfax, University of Sfax, 3038, Sfax, Tunisia ' Laboratory of Electronics and Technologie of Information, National Engineering School of Sfax, University of Sfax, 3038, Sfax, Tunisia ' Laboratory of Electronics and Technologie of Information, National Engineering School of Sfax, University of Sfax, 3038, Sfax, Tunisia ' Laboratory of Electronics and Technologie of Information, National Engineering School of Sfax, University of Sfax, 3038, Sfax, Tunisia

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.

Keywords: lossless image compression; sparse histogram; histogram packing; predictive coding; JPEG-LS; CALIC; JPEG 2000.

DOI: 10.1504/IJSISE.2020.113562

International Journal of Signal and Imaging Systems Engineering, 2020 Vol.12 No.1/2, pp.28 - 39

Accepted: 01 Sep 2020
Published online: 08 Mar 2021 *

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