Boosting vector quantization technique using fast directional shrinking search optimization (DSSO) algorithm

• Published in: AIP conference proceedings Vol. 2375; no. 1
• Main Authors: Tripathia, Durga Prasad, Pardha Saradhia, P., Madhava, B. T. P., Jenab, U. R., Sateesh Kumar, D.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Melville American Institute of Physics 05.10.2021
• Subjects: Algorithms; Computational efficiency; Computing time; Heuristic methods; Image compression; Optimization; Particle swarm optimization; Searching; Vector quantization
• ISSN: 0094-243X; 1935-0465; 1551-7616; 1551-7616
• DOI: 10.1063/5.0066710

A new fast vector quantization along with a novel fast heuristic optimization algorithm has been proposed in this paper that efficiently boosts the process of finding the global optimum region in the search region. Stochastic variables are used in this algorithm to excel the speed of exploration and exploitation and at the same time making the whole process of vector quantization (VQ) computationally efficient. The search domain gradually shrinks to make algorithm effective and efficient. On comparing the simulation results of VQ based on particle swarm optimization (PSO), quantum particle swarm optimization (QPSO) and firefly algorithm (FFA) using some well- known benchmark functions has validated the efficiency of the algorithm. In finding the global optimum point, the proposed algorithm decreases the computing time 44 % relative to others. Vector quantization based image compression is a NP-hard problem has been successfully and efficiently solved in this proposed work.