Wavelet Neural Network Image Compression

As an efficient data compression technique, vector quantization(VQ) has been widely applied to image compression. This paper presents a systematic introduction to the theory of VQ data compression, focusing on the key technology of VQ——codebook design algorithm:The classical algorithm of VQ codebook design—LBG algorithm is reviewed. The VQ codebook design algorithm based on the competed artificial neural network—wavelet neural network and LVQ neural network are studied.The training process and performance of these three codebook design algorithms are studied through simulation experiments. During the experiments, image blocking is adopted in the construction of the vectors.An improved method for codebook design based on the combination of wavelet and LBG is put forward according to the characteristics of LBG algorithm and its validity is validated by the experiment.The experimental results indicate that the LBG algorithm and LVQ algorithm are sensitive dependence to initial codebook; Wavelet algorithm takes a long training time. For a given size of codeword, a larger codebook will result in a lower compression ratio but a better quality of reconstructed image. When using the same codebook, the coding efficiency of less codeword performs better.