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K-Means Color Image Quantization with Deterministic Initialization: New Image Quality Metrics

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Image Analysis and Recognition (ICIAR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10882))

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Abstract

Color image quantization is used in several tasks of color image processing as an image segmentation, image compression, image watermarking, etc. In this paper we consider four traditional (MSE, PSNR, DE76 and DM) and four new perceptual metrics (DSCSI, HPSI, MDSIs and MDSIm) as useful tools for evaluating quantized images. The values of these metrics confirm that Wu’s algorithm can be used as effective deterministic initialization of K-Means method. No empty clusters are produced by this method of quantization. The experiments were realized using 24 benchmark color images for different numbers of quantization levels. The same quantization with additional Floyd-Steinberg dithering generates the images with even better values of tested perceptual metrics.

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Acknowledgments

This work was supported by the Polish Ministry for Science and Education under internal grant BK-204/RAU1/2017/t-4 for the Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland.

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Authors and Affiliations

  1. Silesian University of Technology, ul. Akademicka 16, 44-100, Gliwice, Poland

    Mariusz Frackiewicz & Henryk Palus

Authors
  1. Mariusz Frackiewicz
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  2. Henryk Palus
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Correspondence to Mariusz Frackiewicz .

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Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

  3. Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Bart ter Haar Romeny

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Frackiewicz, M., Palus, H. (2018). K-Means Color Image Quantization with Deterministic Initialization: New Image Quality Metrics. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_7

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  • DOI: https://doi.org/10.1007/978-3-319-93000-8_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92999-6

  • Online ISBN: 978-3-319-93000-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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