A new fast codeword search algorithm for imagc vcctor quantisation (VQ) is introduced. This algorithm performs a fast codeword search in the Hadamard transform (HT) domain using the partial distance search (PDS) technique, Experimental results show that the algorithm needs only 2-3% of the distortion calculations of the exhaustive search method.

A multiresolution watermarking method with grey-level watermarking is presented. Both the watermark and the original image are decomposed into three-level multiresolution structures with different mechanisms. The decomposed watermark information is further coded by error correction coding. Each level of watermark information is embedded into the corresponding level of the transformed image. Experimental results show that the proposed algorithm is robust against common image manipulations.

Block truncation coding (BTC) is an efficient block coding technique suitable for real -time image compression, and it has high channel error re-sisting capability and good reconstructed image qual-ity. Abaolute moment BTC (AMBTC) is a simple and fast variant of BTC. The main shortcoming of the original AMBTC algorithm is the high bit rate (nor-mally 2bits/pixel). In order to reduce the bit rate of AMBTC, an efficient BTC image compression algo-rithm with vector quantization (VQ) is presented in this paper. The main idea of the proposed algorithm is to reduce the number of bits required to code the higher mean, the lower mean and the bit plane, which are created by AMBTC for each image block. On the one hand, a simple look-up-table method is presented for coding the higher mean and the lower mean of a block. On the other hand, vector quantization tech-nique is introduced to reduce the number of bits used to code the bit plane. Test results prove the effective-ness of the proposed algorithm.

Summary A novel fast KNN classification algorithm is proposed for pattern recognition. The technique uses one important feature, mean of the vector, to reduce the search space in the wavelet domain. Since the proposed algorithm rejects those vectors that are impossible to be the k closest vectors in the design set, it largely reduces the classification time and holds the classification performance as that of the original classification algorithm. The simulation on texture image classification confirms the efficiency of the proposed algorithm.

The fast codeword search algorithm based on the mean pyramids of codewords is currently used in image coding applications. A more efficient algorithm is presented for fast codeword searching which is based on the use of mean-variance pyramids of codewords. Experimental results confirm the effectiveness of the proposed method.