Fast search algorithms for ECVQ using projection pyramids and variance of codewords

金沢大学大学院自然科学研究科情報システム金沢大学工学部Vector quantization for image compression requires expensive time to find the closest codeword through the codebook. Codebook design based on empirical data for entropy-constrained vector quantization (ECVQ) involves a time consuming training phase in which a Lagrangian cost measure has to be minimized over the set of codebook vectors. In this paper, we propose two fast codebook generation methods for ECVQ. In the first one, we use an appropriate topological structure of input vectors and codewords to reject many codewords that are impossible to be candidates for the best codeword. In the second method, we use the variance test to increase the ability of the first algorithm to reject more codewords. These algorithms allow significant acceleration in the codebook design process. Experimental results are presented on image block data. These results show that our new algorithms perform better than the previously known methods

Implementation of Vector Quantization for Image Compression - A Survey

This paper presents a survey on vector quantization for image compression. Moreover it provides a means of decomposition of the signal in an approach which takes the improvement of inter and intra band correlation as more lithe partition for higher dimension vector spaces. Thus, the image is compressed without information loss using artificial neural networks (ANN). Since 1988, a growing body of research has examined the use of VQ for the image compression. This paper discusses about vector quantization, its principle and examples, its various techniques and image compression its advantages and applications. Additionally this paper also provides a comparative table in the view of simplicity, storage space, robustness and transfer time of various vector quantization methods. In addition the proposed paper also presents a survey on different methods of vector quantization for image compression