A stable and fast PSO algorithm guided by SPSA for vector quantization - based image compression.
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| Title: | A stable and fast PSO algorithm guided by SPSA for vector quantization - based image compression. |
|---|---|
| Alternate Title: | Vector nicemleme tabanlı görüntü sıkıştırma için EPSY ile yönlendirilen kararlı ve hızlı bir PSO algoritması. |
| Authors: | Kılıç, İlker1 ilker.kilic@cbu.edu.tr, Sarnel, Haldun1 haldun.sarnel@cbu.edu.tr |
| Source: | Pamukkale University Journal of Engineering Sciences. 2025, Vol. 31 Issue 7, p1255-1267. 13p. |
| Subjects: | Image compression, Vector quantization, Stochastic approximation, Mathematical optimization, Metaheuristic algorithms, Particle swarm optimization |
| Abstract (English): | 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. [ABSTRACT FROM AUTHOR] |
| Abstract (Turkish): | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Pamukkale University Journal of Engineering Sciences is the property of Pamukkale University Journal of Engineering Sciences (PAJES) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 190434088 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A stable and fast PSO algorithm guided by SPSA for vector quantization - based image compression. – Name: TitleAlt Label: Alternate Title Group: TiAlt Data: Vector nicemleme tabanlı görüntü sıkıştırma için EPSY ile yönlendirilen kararlı ve hızlı bir PSO algoritması. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kılıç%2C+İlker%22">Kılıç, İlker</searchLink><relatesTo>1</relatesTo><i> ilker.kilic@cbu.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Sarnel%2C+Haldun%22">Sarnel, Haldun</searchLink><relatesTo>1</relatesTo><i> haldun.sarnel@cbu.edu.tr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Pamukkale+University+Journal+of+Engineering+Sciences%22">Pamukkale University Journal of Engineering Sciences</searchLink>. 2025, Vol. 31 Issue 7, p1255-1267. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+compression%22">Image compression</searchLink><br /><searchLink fieldCode="DE" term="%22Vector+quantization%22">Vector quantization</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+approximation%22">Stochastic approximation</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (Turkish) Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Pamukkale University Journal of Engineering Sciences is the property of Pamukkale University Journal of Engineering Sciences (PAJES) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.5505/pajes.2025.78006 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1255 Subjects: – SubjectFull: Image compression Type: general – SubjectFull: Vector quantization Type: general – SubjectFull: Stochastic approximation Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Particle swarm optimization Type: general Titles: – TitleFull: A stable and fast PSO algorithm guided by SPSA for vector quantization - based image compression. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kılıç, İlker – PersonEntity: Name: NameFull: Sarnel, Haldun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13007009 Numbering: – Type: volume Value: 31 – Type: issue Value: 7 Titles: – TitleFull: Pamukkale University Journal of Engineering Sciences Type: main |
| ResultId | 1 |
