Withdrawn: 75 Model-based Color Quantization of Images, with Spatial Component, using the EM Algorithm

Thomas M. Menino Convention & Exhibition Center 

Color quantization is a technique used in Computer Imaging in which the number of true colors in an RGB image is reduced to a prespecified number of colors in a color palette, without appreciably reducing image quality. The importance of this technique lies in the fact that a color-quantized image can be displayed on devices or software platforms that are not fully capable of rendering all the colors of an image. Another important aspect of color quantization is that it facilitates image compression, owing to the limited colors in the color palette used to display images. In this work, we perform model-based color quantization that accurately captures the RGB channels of a two-dimensional digital image. Our model employs a Gaussian Copula Model with a Kumaraswamy Distribution as the marginal distribution to perform color quantization using the EM algorithm. In addition, we describe an updated model that incorporates spatial context into the color quantization process via penalization, providing an alternative approach. We test this model on a set of 2D digital images to assess the accuracy and performance for both the non-spatial and spatial component models.

Gaussian copula model

Kumaraswamy distribution

Expectation-Maximization algorithm

Spatial component

K-means clustering

Visual-information-Fidelity (VIF)