2015 International Symposium of Computer Application and Information Technology (IsCait 2015)

203 / 1971-01-01 00:00:00

A Low Complexity Quantization Scheme Based On Classified Multi-objective Prediction

5509

Wavelet decomposition in Image coding allows for efficient coding matched to the statistics of each frequency subband and to the characteristics of the human visual system. Vector quantization (VQ) provides many attractive features for image coding with high compression ratios. However, high computational complexity becomes the drawback of image coding with VQ. To address this problem, in this paper, we have proposed a novel vector quantization algorithm based on classified multi-objective prediction (CMOP). In CMOP, the codebook is generated adaptively according the similarity of coefficient block in the same frequency subband. It overcomes the computational complexity bottleneck of other vector training with codebook. The experimental results revealed that the proposed algorithm produced a high compression ratio with minimum loss. To achieve the best performance of image coding, we proposed the optimal wavelet basis for CMOP quantization algorithm.