Vector quantization using the improved differential evolution algorithm for image compression

Vector quantization (VQ) is a popular image compression technique with a simple decoding architecture and high compression ratio. Codebook designing is the most essential part in vector quantization. Linde–Buzo–Gray (LBG) is a traditional method of generation of VQ codebook which results in lower PS...

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Veröffentlicht in:Genetic programming and evolvable machines Jg. 20; H. 2; S. 187 - 212
1. Verfasser: Nag, Sayan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.06.2019
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Biomedical Engineering and Bioengineering
Boundary control
Compilers
Compression ratio
Computer Science
Computing time
Decoding
Electrical Engineering
Evolutionary algorithms
Evolutionary computation
Image compression
Image enhancement
Image quality
Image reconstruction
Interpreters
Optimization
Optimization techniques
Programming Languages
Programming Techniques
Scaling factors
Software Engineering/Programming and Operating Systems
Vector quantization
Bat algorithm (BA)
Codebook
Vector quantization
Improved differential evolution (IDE) algorithm
Improved particle swarm optimization (IPSO) algorithm
Image compression
Firefly algorithm (FA)
Linde–Buzo–Gray (LBG) algorithm
ISSN:1389-2576, 1573-7632
Online-Zugang:Volltext
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Zusammenfassung:Vector quantization (VQ) is a popular image compression technique with a simple decoding architecture and high compression ratio. Codebook designing is the most essential part in vector quantization. Linde–Buzo–Gray (LBG) is a traditional method of generation of VQ codebook which results in lower PSNR value. A codebook affects the quality of image compression, so the choice of an appropriate codebook is a must. Several optimization techniques have been proposed for global codebook generation to enhance the quality of image compression. In this paper, a novel algorithm called IDE-LBG is proposed which uses improved differential evolution algorithm coupled with LBG for generating optimum VQ codebooks. The proposed IDE works better than the traditional DE with modifications in the scaling factor and the boundary control mechanism. The IDE generates better solutions by efficient exploration and exploitation of the search space. Then the best optimal solution obtained by the IDE is provided as the initial codebook for the LBG. This approach produces an efficient codebook with less computational time and the consequences include excellent PSNR values and superior quality reconstructed images. It is observed that the proposed IDE-LBG find better VQ Codebooks as compared to IPSO-LBG, BA-LBG and FA-LBG.
Bibliographie:ObjectType-Article-1
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ISSN:1389-2576
1573-7632
DOI:10.1007/s10710-019-09342-8