Enhanced embedded zerotree wavelet algorithm for lossy image coding. Issue 8 (29th May 2019)

• Record Type: Journal Article
• Authors: Boujelbene, Rania; Boubchir, Larbi; Ben Jemaa, Yousra
• Is Part Of: IET image processing. Volume 13:Issue 8(2019)
• Page Start: 1364
• Page End: 1374
• Publication Date: 2019-05-29
• Subjects: wavelet transforms -- binary codes -- trees (mathematics) -- data compression -- image coding; peak‐signal‐to‐noise ratio -- lossy image compression -- binary coding -- conventional EZW -- improved EZW schemes -- natural image coding applications -- medical image coding applications -- lossy image coding -- embedded zerotree wavelet algorithm -- effective coding technique -- low‐bit‐rate image compression -- enhanced EZW -- high compression performance -- hierarchical trees -- JPEG2000 -- SPIHT; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.6052
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Embedded zerotree wavelet (EZW) algorithm is the well‐known effective coding technique for low‐bit‐rate image compression. In this study, the authors propose a modification of this algorithm, namely new enhanced EZW (NE‐EZW), allowing to achieve a high compression performance in terms of peak‐signal‐to‐noise ratio and bitrate for lossy image compression. To distribute probabilities in a more efficient way, the proposed approach is based on increasing the number of coefficients not to be encoded by the use of new symbols. Furthermore, the proposed method optimises the binary coding by the use of the compressor cell operator. Experimental results demonstrated the effectiveness of the proposed scheme over the conventional EZW and other improved EZW schemes for both natural and medical image coding applications. They have also shown that the proposed approach outperforms the most well‐known algorithms, namely set partitioning in hierarchical trees (SPIHT) and JPEG2000.