Image compression based on vector quantization and optimized code-book design using Genetic Mating Influenced Slime Mould (GMISM) algorithm

Abstract

Large amounts of storage are required to store the recent massive influx of fresh photographs that are uploaded to the internet. Many analysts created expert image compression techniques during the preceding decades to increase compression rates and visual quality. In this research work, a unique image compression technique is established for Vector Quantization (VQ) with the K-means Linde–Buzo–Gary (KLBG) model. As a contribution, the codebooks are optimized with the aid of hybrid optimization algorithm. The projected KLBG model included three major phases: an encoder for image compression, a channel for transitions of the compressed image, and a decoder for image reconstruction. In the encoder section, the image vector creation, optimal codebook generation, and indexing mechanism are carried out. The input image enters the encoder stage, wherein it’s split into immediate and non-overlapping blocks. The proposed GMISM model hybridizes the concepts of the Genetic Algorithm (GA) and Slime Mould Optimization (SMO), respectively. Once, the optimal codebook is generated successfully, the indexing of the every vector with index number from index table takes place. These index numbers are sent through the channel to the receiver. The index table, optimal codebook and reconstructed picture are all included in the decoder portion. The received index table decodes the received indexed numbers. The optimally produced codebook at the receiver is identical to the codebook at the transmitter. The matching code words are allocated to the received index numbers, and the code words are organized so that the reconstructed picture is the same size as the input image. Eventually, a comparative assessment is performed to evaluate the proposed model. Especially, the computation time of the proposed model is 69.11%, 27.64%, 62.07%, 87.67%, 35.73%, 62.35%, and 14.11% better than the extant CSA, BFU-ROA, PSO, ROA, LA, SMO, and GA algorithms, respectively.