A Hybrid Color Quantization Algorithm Incorporating a Human Visual Perception Model. (3rd July 2014)

• Record Type: Journal Article
• Authors: Schaefer, Gerald; Nolle, Lars
• Is Part Of: Computational intelligence. Volume 31:Number 4(2015:Nov.)
• Page Start: 684
• Page End: 698
• Publication Date: 2014-07-03
• Subjects: color quantization -- color palette -- optimization -- simulated annealing -- human perception -- image quality metric; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12043
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract : Color quantization is a common image processing technique where full color images are to be displayed using a limited palette of colors. The choice of a good palette is crucial as it directly determines the quality of the resulting image. Standard quantization approaches aim to minimize the mean squared error (MSE) between the original and the quantized image, which does not correspond well to how humans perceive the image differences. In this article, we introduce a color quantization algorithm that hybridizes an optimization scheme based with an image quality metric that mimics the human visual system. Rather than minimizing the MSE, its objective is to maximize the image fidelity as evaluated by S‐CIELAB, an image quality metric that has been shown to work well for various image processing tasks. In particular, we employ a variant of simulated annealing with the objective function describing the S‐CIELAB image quality of the quantized image compared with its original. Experimental results based on a set of standard images demonstrate the superiority of our approach in terms of achieved image quality.