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

Abstract

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.

Keywords

• Image compression
• Metaheuristic algorithms
• Vector Quantization
• Codebook generation
• PSO
• SPSA

Vector nicemleme tabanlı görüntü sıkıştırma için EPSY ile yönlendirilen kararlı ve hızlı bir PSO algoritması

Abstract

Görüntü sıkıştırma, depolama gereksinimlerini azaltmak ve iletim verimliliğini artırmak açısından büyük bir öneme sahiptir. Vektör nicemleme (VN) tabanlı kayıplı görüntü sıkıştırmanın başarısı, esasen bir optimizasyon problemi olan kod tablosu üretiminin kalitesine bağlıdır. Bu makalede, hem algoritmanın yakınsama hızını hem de VN kod tablosunun kalitesini artırmak için Eşzamanlı Pertürbasyon Stokastik Yaklaşımı (EPSY) tekniğini Parçacık Sürü Optimizasyonu (PSO) ile bütünleştiren hibrit bir algoritma önerilmektedir. Önerilen EPSY-HPSO algoritması, her iterasyonda birden fazla alternatif kod kitabı üreterek en iyisini seçmekte ve yerel minimum noktalarından kaçınarak daha hızlı bir yakınsama sağlamaktadır. Farklı kontrast seviyelerine sahip standart gri seviye görüntüler üzerinde gerçekleştirilen deneysel sonuçlar, EPSY-HPSO algoritmasının hem ortalama kare hata (OKH) değerlerini düşürme hem de daha yüksek yakınsama hızları açısından klasik PSO ve EPSY algoritmalarından daha başarılı olduğunu göstererek VN tabanlı görüntü sıkıştırmadaki üstünlüğünü kanıtlamaktadır. Bu üstünlüğün diğer metasezgisel algoritmalarla karşılaştırıldığında da geçerli olduğu gösterilmektedir

Keywords

• Görüntü sıkıştırma
• Metasezgisel algoritmalar
• Vektör nicemleme
• Kod tablosu üretimi
• PSO
• EPSY

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