Earticle

A method for image compression, adaptive difference compensation vector quantization using dynamic image block adjustment is proposed in this paper. Before image coding, this proposed method analyzes the similarity values between the encoding image sub-block and its 8-neighbor sub-blocks, then determines the encoding rule according to the similarity value and the preset threshold. If the similarity value is approximate to the threshold, we use the same codeword to encode the neighbor block and the encoding image block; otherwise, the neighbor block is separately encode. When encoding, the difference between each image block and its matching codeword is first computed to obtain the difference image, and then the sign bits of the pixel difference is imposed the running length coding on and attached after the codeword index. When decoding, this proposed method restores the compressed image according to the codeword index, performs the running length encoding to deal with decode the attached information, uses the window of 33 size to tackle the adaptive difference compensation and derive the final decoding image. The experiment results reveal that this proposed method can improve the encoding speed and image restoration performance against the normal vector quantization.

In this paper, we propose a new method for reducing the number of distance calculations in the LBG (Linde, Buzo, Gray) algorithm, which is widely used method to construct a codebook in vector quantization of speech recognition system. The proposed algorithm can reduce the distance calculation between input vector and codeword by utilizing the observation that codewords are quickly stabilized as the number of iteration increases. From the simulation results, it is shown that we can reduce the running times over $43.77\%$ on average in comparison with current LBG algorithm without sacrificing the performance of codebook.