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research-article

Modified vector quantization algorithm to overcome the blocking artefact problem of vector quantization algorithm

Authors: Abul Hasnat, Dibyendu Barman, Santanu Halder, Debotosh BhattacharjeeAuthors Info & Claims

Journal of Intelligent & Fuzzy Systems, Volume 32, Issue 5

Pages 3711 - 3727

https://doi.org/10.3233/JIFS-169304

Published: 01 January 2017 Publication History

Abstract

The present work proposes a modified vector quantization algorithm to overcome the blocking artifacts problem of the conventional Vector Quantization (VQ) algorithm. Blocking artifacts affects visual appeal of the decompressed images. The Vector Quantization (VQ) algorithm is improvised where the blocking artifact does not appear in the decompressed image. The proposed algorithm is applied on luminance-chrominance color model where luminance channel is compressed using a novel approach. For the luminance channel, eight separate clusters are constructed using the k-means clustering algorithm then for each cluster, fuzzy intensification applied separately; next for each cluster training vectors are formed by taking sixteen consecutive elements from a cluster to form one vector, next sixteen elements for second vector and so on. For each group of these training vectors, vector quantization is applied to generate the code vectors. For chrominance channels the conventional VQ algorithm is applied. At the time of decompression the reverse process is followed. The modified VQ algorithm has been applied on standard UCID v.2 image database and standard images found in literature where blocking artifacts problem is effectively solved. Experimental result shows that the proposed algorithm successfully avoids the blocking artefacts and the quality of the decompressed image is improved in terms of PSNR and vSNR compared to the conventional VQ algorithm. This article focuses on retaining more original information of the image rather than restoration of the decompressed image where blocking artifacts exists.

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Cited By

Barman DHasnat ABarman B(2022)Development of Multi-Image Compression Technique Based on Common Code VectorSN Computer Science10.1007/s42979-022-01450-04:1Online publication date: 24-Oct-2022
https://dl.acm.org/doi/10.1007/s42979-022-01450-0
Barman DHasnat ABarman B(2022)A quantization based codebook formation method of vector quantization algorithm to improve the compression ratio while preserving the visual quality of the decompressed imageMultidimensional Systems and Signal Processing10.1007/s11045-022-00856-634:1(127-145)Online publication date: 26-Oct-2022
https://dl.acm.org/doi/10.1007/s11045-022-00856-6
Hasnat ABarman DBatyrshin ICross VKreinovich VRifqi M(2019)A proposed multi-image compression techniqueJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-1836036:4(3177-3193)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/JIFS-18360

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Modified vector quantization algorithm to overcome the blocking artefact problem of vector quantization algorithm

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Published In

cover image Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology Volume 32, Issue 5

Recent advancements in computer, communication and computational sciences

2017

542 pages

ISSN:1064-1246

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IOS Press and the authors. All rights reserved.

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IOS Press

Netherlands

Publication History

Published: 01 January 2017

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Cited By

Barman DHasnat ABarman B(2022)Development of Multi-Image Compression Technique Based on Common Code VectorSN Computer Science10.1007/s42979-022-01450-04:1Online publication date: 24-Oct-2022
https://dl.acm.org/doi/10.1007/s42979-022-01450-0
Barman DHasnat ABarman B(2022)A quantization based codebook formation method of vector quantization algorithm to improve the compression ratio while preserving the visual quality of the decompressed imageMultidimensional Systems and Signal Processing10.1007/s11045-022-00856-634:1(127-145)Online publication date: 26-Oct-2022
https://dl.acm.org/doi/10.1007/s11045-022-00856-6
Hasnat ABarman DBatyrshin ICross VKreinovich VRifqi M(2019)A proposed multi-image compression techniqueJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-1836036:4(3177-3193)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/JIFS-18360

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