Maximun Likelihood Clustering Method Based On Color Features

A novel maximum likelihood estimation based on features for color image pixels clustering is proposed. A 3-D color feature data space is spanned to show the pixel frequency of color R, G and B. The clustering based on features can be considered as normal mixture distribution parameter estimation by using maximum likelihood estimation based on features instead of on samples is proposed. An image based on features means that the image is presented by empirical distribution function or special histogram, which includes all possible colors even some color features are absent. It is implied that an IDD (Independent and Identically Distributed) is assumed, The empirical distribution function is formed by Bernoulli random variable. It is shown that the statistics of maximum likelihood estimation based on features are better, because these statistics are sufficient and complete. The clustering based on features can be used for multi-dimensional and multi-class histogram parameter estimation

Optimal Y-U-V Model Based On Karhunen-Loeve

An optimal Y-U-V transformation based on Karhunen-Loeve transformation for image compression proposed in this paper is considered as a spectral redundancy reduction. The PSNR is gained for optimal Y-U-V in comparison with traditional fixed Y-U-V transformation, because the variances are most separately after K-L transformation and the down sampling is taken on the coordinates with smallest variances in optimal Y-U-V transformation. The K-L transformation in optimal Y-U-V is an image-dependent transform that is to de-correlate the data in color spectral domain by using eigenvector matrix of covariance of the colors of the image. A normalization matrix follows the optimal Y-U-V transformation is used for ranging the data of Y-U-V within 0-255. The procedure is used in encoding only and the Y-U-V transformation can be transmitted with compressed data and de-coding easily