Fast Adaptive Algorithm for Time-Critical Color Quantization Application

Abstract. Color quantization is the process of grouping n data points to k cluster. We proposed a new approach, based on Wu’s color quantization [6]. Our approach can significantly reduce the time consumption during the process compared with available methods but still maintain an acceptable quality of color distribution. As a rough rule of thumb [4], a quantized image with more than 30 dB of PSNR is often indistinguishable from the uncompressed original image. To achieve this requirement, we proposed to put the cutting plane through the centroid of the largest value representing variance box on the 3Dcolor histogram of color distribution. This plane is perpendicular to the axis, on which the sum of the squared Euclidean distances between the centroid of both sub-boxes and the centroid of the box is greatest. This guarantees that the total variances of sub-boxes are reduced automatically. To speed up the process, we exploited the dynamic programming as Wu [6] used in his approach. Unlike Wu’s approach, we replaced the second order moment calculation with a value representing variance. Because variance is not actually used in calculation, a simpler indicator of data scatterness would speed up the process. From our whole process, we achieved approximately 40 % less time consumption than Wu's quantizer [6].

Fast adaptive algorithm for time-critical color quantization application,Proc. VIIth Digital Image Computing Sydney: Techniques and Applications 2003; 781-785 Authors Dr. P.Alli received her Ph.D degree in Computer Sciencefrom Madurai Kamaraj

Abstract. Color quantization is the process of grouping n data points to k cluster. We proposed a new approach, based on Wu's color quantizatio

Optimal image compression via block-based adaptive colour reduction with minimal contour effect

Current image acquisition devices require tremendous amounts of storage for saving the data returned. This paper overcomes the latter drawback through proposing a colour reduction technique which first subdivides the image into patches, and then makes use of fuzzy c-means and fuzzy-logic-based inference systems, in order to cluster and reduce the number of the unique colours present in each patch, iteratively. The colours available in each patch are quantised, and the emergence of false edges is checked for, by means of the Sobel edge detection algorithm, so as to minimise the contour effect. At the compression stage, a methodology taking advantage of block-based singular value decomposition and wavelet difference reduction is adopted. Considering 35000 sample images from various databases, the proposed method outperforms centre cut, moment-preserving threshold, inter-colour correlation, generic K-means and quantisation by dimensionality reduction