An efficient encoding algorithm for vector quantization based on subvector technique

An Efficient Palette Generation Method for Color Image Quantization

This article describes an efficient method to generate a color palette for color image quantization. The method consists of two stages. In the first stage, the initial palette is generated. Initially, the color palette is an empty set. First, the N colors are generated according to the data distribution of the input image in the RGB (Red, Green, Blue) color space. Then, one color is selected from the N colors and this color is added to the initial palette, and the step is repeated until the color number of the initial palette is equal to K. In the second stage, the quantized image is generated using the fast K-means algorithm. There are many sampling rates used in this study. For each sampled pixel, a fast searching method is employed to efficiently determine the closest color in the palette. Experimental results show that the high-quality quantized images can be generated by the proposed method. When the sampling rate equals 0.125, the computation time of the proposed method is less than 0.3 s for all cases.

Codewords Distribution-Based Optimal Combination of Equal-Average Equal-Variance Equal-Norm Nearest Neighbor Fast Search Algorithm for Vector Quantization Encoding

Vector quantization encoding requires expensive time to find the closest codeword through the codebook for each input vector. A fast search algorithm for encoding is proposed in this paper. The multilevel elimination criterion is still derived from the three features (mean, variance, and norm) inequalities constraints, but the order of the three inequalities constraints, instead of the predefined order like other conventional multilevel elimination criterion, is optimized to speed up the encoding procedure. In the proposed algorithm, the elimination criterion at the first level is set to mean inequality constraint because of its narrower search width, and the priority order at the second and/or third level of variance and norm inequalities constraints is optimized based on the codewords distribution and the location of input vector in terms of the considered features. The experimental results demonstrate the effectiveness of the proposed algorithm.

Fast-searching algorithm for vector quantization using projection and triangular inequality

In this paper, a new and fast-searching algorithm for vector quantization is presented. Two inequalities, one used for terminating the searching process and the other used to delete impossible codewords, are presented to reduce the distortion computations. Our algorithm makes use of a vector's features (mean value, edge strength, and texture strength) to reject many unlikely codewords that cannot be rejected by other available approaches. Experimental results show that our algorithm is superior to other algorithms in terms of computing time and the number of distortion calculations. Compared with available approaches, our method can reduce the computing time and the number of distortion computations significantly. Compared with the best method of reducing distortion computation, our algorithm can further reduce the number of distortion calculations by 29% to 58.4%. Compared with the best encoding algorithm for vector quantization, our approach also further reduces the computing time by 8% to 47.7%.