Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression

TL;DR

This paper introduces a novel optimization algorithm, IDE-LBG, combining Improved Differential Evolution with LBG for generating superior codebooks in vector quantization, leading to higher image quality and reduced computational time.

Contribution

The paper proposes a new hybrid algorithm, IDE-LBG, that outperforms existing methods in generating optimal codebooks for image compression.

Findings

IDE-LBG produces higher PSNR values than traditional methods.

The approach results in better reconstructed image quality.

It reduces computational time compared to other optimization techniques.

Abstract

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. LindeBuzoGray, 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.