Color image quantization using Gaussian Particle Swarm Optimization(CIQ-GPSO)

This article proposes a color image quantization algorithm based on Gaussian Particle Swarm Optimization (GPSO). PSO is a population-based optimization algorithm modeled after the simulation of social behavior of swarms to find near-optimal solutions. The algorithm randomly initializes each particle...

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Bibliographic Details
Published in2016 International Conference on Inventive Computation Technologies (ICICT) Vol. 1; pp. 1 - 4
Main Authors Barman, Dibyendu, Hasnat, Abul, Sarkar, Suchintya, Rahaman, Md Atiqur
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2016
Subjects
Clustering algorithms
Color
Color map
Color quantization
Gaussian function
Image color analysis
MSE
Optimization
Particle swarm optimization
Quantization (signal)
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Summary:This article proposes a color image quantization algorithm based on Gaussian Particle Swarm Optimization (GPSO). PSO is a population-based optimization algorithm modeled after the simulation of social behavior of swarms to find near-optimal solutions. The algorithm randomly initializes each particle in the swarm to contain K centroids (i.e. color triplets). The K-means clustering algorithm is then applied on each particle to refine the chosen centroids at user specified probability. Each pixel is assigned to the cluster with the closest centroid. Next the Gaussian PSO is applied to update the centroids obtained using the K-means clustering. For performance analysis the proposed algorithm is tested on standard images in the literature and experimental result shows that the Gaussian PSO based quantization method improves image quality significantly compared to conventional PSO based approach.
DOI:10.1109/INVENTIVE.2016.7823295