Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression
This work addresses the need for efficient image compression techniques with improved quality, though it is incremental as it builds on existing optimization methods for codebook generation.
The paper tackles the problem of generating high-quality codebooks for vector quantization in image compression by proposing the IDE-LBG algorithm, which combines an improved differential evolution algorithm with LBG to achieve better PSNR values and faster computational times compared to existing methods like IPSO-LBG, BA-LBG, and FA-LBG.
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.