Basic and Perceptual Color Quantization with Nature Inspired Computing
A Novel Approach to Color Quantization using Intelligent Water Drops and Differential Evolution algorithms
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In this work, two different nature inspired optimization algorithms are applied to the Color Quantization (CQ) problem. CQ is a technique that is used to decrease the number of distinct colors in an image. In many applications, an image with a limited number of colors provides more effective implementations either in software or hardware. However, this process reduces the visual quality of the image. Therefore, one of the major concerns in CQ is keeping the distortion of the quantized image as low as possible. CQ is considered as an NP-Hard optimization problem. In the first part of this study...
In this work, two different nature inspired optimization algorithms are applied to the Color Quantization (CQ) problem. CQ is a technique that is used to decrease the number of distinct colors in an image. In many applications, an image with a limited number of colors provides more effective implementations either in software or hardware. However, this process reduces the visual quality of the image. Therefore, one of the major concerns in CQ is keeping the distortion of the quantized image as low as possible. CQ is considered as an NP-Hard optimization problem. In the first part of this study, for the first time, Intelligent Water Drops (IWD) algorithm is adopted to Basic CQ. The comparison results against eight approaches revealed that IWD-CQ algorithm achieves promising results in terms of the visual quality of quantized images. Second part of the study, investigates a Perceptual CQ approach, DE-PCQ, which is based on a model of Human Vision System (HVS). The model aims to simulate the property of human s eye in perceiving an average color of the adjacent pixels. Comparison results demonstrate distinct superiority of DE-PCQ in terms of the perceptual quality of quantized images.
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