Abstract
Vector quantization (VQ) is one of the most often discussed methods in scientific literature on image data compression [95], [103], [126], [171], [194], [329]. The idea of applying VQ to image compression relies on replacing separable image blocks with some class of model blocks which are the most representative blocks for this image. Since human eye perception is based on seeing bigger image parts, changes made by the vector quantization method may not be detected at all. In this chapter we first explain how to prepare an image for vector quantization. Next the image compression problem is explained and the VQ algorithm based on neural networks is described.
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© 2004 Springer-Verlag Berlin Heidelberg
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Rutkowski, L. (2004). Vector Quantization for Image Compression. In: New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing. Studies in Fuzziness and Soft Computing, vol 143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-40046-2_7
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DOI: https://doi.org/10.1007/978-3-540-40046-2_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05820-2
Online ISBN: 978-3-540-40046-2
eBook Packages: Springer Book Archive