A reinforcement-learning approach to color quantization

Chien Hsing Chou, Mu Chun Su, Yu Xiang Zhao, Fu Hau Hsu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish
Pages (from-to)141-150
Number of pages10
JournalTamkang Journal of Science and Engineering
Issue number2
StatePublished - Jun 2011


  • Classifier systems
  • Color quantization
  • Color reduction
  • Machine learning
  • Neuro-Fuzzy systems
  • Pattern recognition
  • Reinforcement learning


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