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Activity Number: 659
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Imaging
Abstract #321478
Title: Improving K-Means Color Quantization of Images with the Use of Adaptive Nonlinear Transformations in Different Colorspaces
Author(s): Ranjan Maitra* and Juan Pablo Rodriguez-Ramirez
Companies: Iowa State University and Iowa State University
Keywords: colorspace ; image compression ; color quantization ; preceptual uniformity ; k-means ; color palette

Color in any pixel of a digital image is represented in the RGB model in terms of combinations of the three primary colors, namely red, blue and green. Alternative models that provide equivalent representations are the tristimulus XYZ, LUV, HSB/HSL (hue, saturation, brightness/lightness), HCL (hue, chroma, luminosity) colorpaces and so on. These representations of color are bijective from one colorspace to the other, however being nonlinearly related, each representation is not uniform in terms of perception. We investigate the performance of k-means color quantization algorithms in each of these spaces and provide an adaptive approach for the best (in terms of the widely-used peak-to-signal noise ratio) lossy representation of such images.

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