Image Subband Coding using Block Adaptive Vector Quantization

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High efficiency image coding using wavelet transform is investigated. The original image is decomposed into 7 subbands by 2-level tree-structured filter bank. Vector quantization is applied to the high frequency subbands, while the lowest subband is coded by 2D-DPCM.Several methods of vector quantization are investigated as follows :(1) All subband type and resolution level by level type : In the former, each vector consists of 15 samples picking from all subbands and the latter consists of two kinds of vectors, one is 3 dimensional for level 2 and the other 12 dimensional for level 1. For both types, we classify vectors into three classes by their powers, each of which reflects the feature of its corresponding pixel block. Each vector is vector-quantized by its class codebook. Both types have nearly the same coding performance, but the latter is superior to the former in having less amounts of computation.(2) Directionally grouped subband type : All subbands are grouped into 3 groups, i. e., horizontal vertical and diagonal. In each group, 5 dimensional vectors are formed and classified into 3 classes by their vector powers. Then each group-class vector is vector quantized by its own code book. This method results as good performance as level by level type between 0.4-0.7bpp. For the rate higher than 0.7bpp, directionally grouped type is superior and for the rate lower than 0.4bpp, all subband type is superior to others.(3) Significant sample type : A vector is formed with n consequtive significant samples in horizontal scanning, whose amplitudes are greater than a threshold. Then, in order to compress the address information of those significant samples, we do decision whether effective or not by every sample block instead of every single sample. This scheme is superior to the above described methods in coding performance.