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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/55826


    Title: A Reinforcement-Learning Approach to Color Quantization
    Authors: Chou, Chien-Hsing;Su, Mu-Chun;Zhao, Yu-Xiang;Hsu, Fu-Hau
    Contributors: 淡江大學電機工程學系
    Keywords: Color Quantization;Color Reduction;Classifier Systems;Pattern Recognition;Reinforcement Learning;Neuro-Fuzzy Systems;Machine Learning
    Date: 2011-06-01
    Issue Date: 2011-08-28 16:43:22 (UTC+8)
    Publisher: 新北市:淡江大學
    Abstract: Color quantization is a process of sampling three-dimensional color space (e.g. RGB) to reduce the number of colors in a color image. By reducing to a discrete subset of colors known as a color codebook or palette, each pixel in the original image is mapped to an entry according to these palette colors. In this paper, a reinforcement-learning approach to color image quantization is proposed. Fuzzy rules, which are used to select appropriate parameters for the adaptive clustering algorithm applied to color quantization, are built through reinforcement learning. By comparing this new method with the original adaptive clustering algorithm on 30 color images, our method shows an improvement of 3.3% to 5.8% in peak signal to noise ratio (PSNR) values on average and results in savings of about 10% in computation time. Moreover, we demonstrate that reinforcement learning is an efficacious as well as efficient way to provide a solution of the learning problem where there is a lack of knowledge regarding the input-output relationship.
    Relation: Tamkang Journal of Science and Engineering=淡江理工學刊 14(2), pp.141-150
    DOI: 10.6180/jase.2011.14.2.07
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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