This Letter presents a fast codeword search algorithm based on ordered Hadamard transform. Before encoding, the ordered Hadamard transform is performed offline on all codewords. During the encoding process, the ordered Hadamard transform is first performed on the input vector, and then a new inequality based on characteristic values of transformed vectors is used to reject the unlikely transformed codewords. Experimental results show that the algorithm outperforms many newly presented algorithms in the case of high dimensionality, especially for high-detail images.

A fast nearest neighbor codeword search algorithm for vector quantization (VQ) is introduced. The algorithm uses three significant features of a vector, that is, the mean, the variance and the norm, to reduce the search space. It saves a great deal of computational time while introducing no more memory units than the equal-average equal-variance codeword search algorithm. With two extra elimination criteria based on the mean and the variance, the proposed algorithm is also more efficient than so-called norm-ordered search algorithm. Experimental results confirm the effectiveness of the proposed algorithm.

This paper presents a novel classified vector quantizer (CVQ) design algorithm which can control the rate and storage size for applications of image coding. In the algorithm, the classification of image blocks is based on the edge orientation of each block in the wavelet domain. The algorithm allocates the rate and storage size available to each class of the CVQ optimally so that the average distortion is minimized. To reduce the arithmetic complexity of the CVQ, we employ a partial distance codeword search algorithm in the wavelet domain. Simulation results show that the CVQ enjoys low average distortion, low encoding complexity, high visual perception quality, and is well-suited for very low bit rate image coding.