Optimization based vector quantization for data reduction in multimedia applications

Abstract

Data reduction and image compression techniques in the present Internet and multi-media age are essential to increase image and video capacity in relation to memory, network bandwidth use and safe data transmission. There have been a different variety of image compression models with varying compression efficiency and visual image quality in the literature. Vector Quantization (VQ) is a widely used image coding scheme that is designed to generate an efficient coding book that includes a list of codewords that assign the input image vector to a minimum dis-tance of Euclidea. The Linde–Buzo–Gray (LBG) historically widely used model produces the local optimal codebook. The LBG model’s codebook architecture is seen as an optimization challenge that can be resolved using metaheuristic algo-rithms. In this perspective, this paper introduces a new DPIO algorithm for code-book generation in VQ. The model presented uses LBG to initialise the DPIO algorithm to construct the VQ technique and is called the DPIO-LBG process. The performance of the DPIO-LBG model is validated with benchmark data and the effects of different performance aspects are investigated. The simulation values showed the DPIO-LBG model to be efficient interfaces of compression efficiency and image quality reconstructed. The presented model produces an efficient code-book with minimum computational time and a better signal to noise ratio (PSNR).