A stable and fast PSO algorithm guided by SPSA for vector quantization-based image compression

Image compression plays a crucial role in reducing storage requirements and improving transmission efficiency. The effectiveness of lossy image compression using vector quantization (VQ) heavily depends on the quality of codebook generation, which is inherently an optimization problem. In this paper, a coupled hybrid algorithm integrating Simultaneous Perturbation Stochastic Approximation (SPSA) into Particle Swarm Optimization (PSO) is proposed to enhance both the convergence speed and codebook quality in vector quantization. The novel SPSA-FPSO algorithm, by generating multiple alternative codebooks at each iteration and selecting the best, successfully avoids local minima and achieves faster convergence. Experimental results, conducted on standard gray-level images of various contrast levels, demonstrate that the proposed SPSA-FPSO algorithm outperforms both basic PSO and SPSA algorithms in terms of lower mean square error (MSE) and higher convergence speeds, establishing its superiority for VQ-based image compression tasks. This superiority is also shown to be valid when compared to other metaheuristic algorithms.