Practical Approaches on Enhancement and Segmentation of Trimulus Color Image with Information Theory Based Quantitative Measuring

Image enhancement and image clustering are two practical implementation approaches for pattern recognition with a variety of engineering applications. In most cases, the actual outcomes of some advanced image processing approaches will directly affect the decision making, such as in target detection and medical diagnosis. Among these approaches, image adaptive contrast stretching is a typical enhancement approach under conditions of improper illumination and unpleasant disturbances, which adapts to the intensity distribution of an image. K-means clustering is a typical segmentation approach to minimize the medium dispersing impact, which produces the distinctive clusters or layers for representing different components of the information being detected. In trimulus color systems, each of three color components takes an independent role along with image processing procedures. To evaluate actual effects of image enhancement and image segmentation, quantitative measures should be taken into account rather than qualitative evaluations exclusively. In this article, quantitative measures for trimulus color systems are proposed instead of the existing gray level ones. Considering the gray level image measures, the corresponding true color RGB component energy, discrete entropy, relative entropy and mutual information are proposed to measure the effectiveness of color image enhancement and segmentation techniques. Key-Words: Image Enhancement, Image Segmentation, Trimulus Color, Energy, Discrete Entropy, Relative Entropy, Mutual Information, Contrast Stretching, K-means Clustering