4 research outputs found
A high-speed codebook design algorithm for ECVQ using angular constraint with search space partitioning
金沢大å¦å¤§å¦é™¢è‡ªç„¶ç§‘å¦ç ”ç©¶ç§‘æƒ…å ±ã‚·ã‚¹ãƒ†ãƒ é‡‘æ²¢å¤§å¦å·¥å¦éƒ¨In this paper, we propose a fast codebook generation algorithm for entropy-constrained vector quantization (ECVQ). The algorithm uses the angular constraint and employs a suitable hyperplane to partition the codebook and image data in order to reduce the search area and accelerate the search process in the codebook design. This algorithm allows significant acceleration in codebook design process. Experimental results are presented on image block data. These results show that our new algorithm performs better than the previously known methods
Fast search algorithms for ECVQ using projection pyramids and variance of codewords
金沢大å¦å¤§å¦é™¢è‡ªç„¶ç§‘å¦ç ”ç©¶ç§‘æƒ…å ±ã‚·ã‚¹ãƒ†ãƒ é‡‘æ²¢å¤§å¦å·¥å¦éƒ¨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
Fast codeword search algorithm for ECVQ using hyperplane decision rule
金沢大å¦å¤§å¦é™¢è‡ªç„¶ç§‘å¦ç ”ç©¶ç§‘æƒ…å ±ã‚·ã‚¹ãƒ†ãƒ é‡‘æ²¢å¤§å¦å·¥å¦éƒ¨Vector quantization is the process of encoding vector data as an index to a dictionary or codebook of representative vectors. One of the most serious problems for vector quantization is the high computational complexity involved in searching for the closest codeword through the codebook. Entropy-constrained vector quantization (ECVQ) codebook design based on empirical data involves an expensive training phase in which Lagrangian cost measure has to be minimized over the set of codebook vectors. In this paper, we describe a new method allowing significant acceleration in codebook design process. This method has feature of using a suitable hyperplane to partition the codebook and image data. Experimental results are presented on image block data. These results show that our method performs better than previously known methods