Image Subband Coding using Block Adaptive Vector Quantization

研究了基于小波变换的高效图像编码方法。原始图像通过2级树型滤波器组分解为7个子带。（1）全子带类型和逐层分辨率：全子带类型由15个采样组成，逐层分辨率由两种矢量组成，一种是3维的第2层矢量，另一种是12维的第1层矢量。对于这两种类型，我们分为三类向量的权力，每一个反映了其对应的像素块的功能。每个矢量由其类别码本进行矢量量化。这两种类型具有几乎相同的编码性能，但后者上级前者具有更少的计算量。(2)方向分组子带类型：所有子带被分组为3个组，即：例如，水平垂直和对角线。在每组中，形成5维向量，并根据向量的幂将其分为3类。然后，每个组类矢量由其自己的码书进行矢量量化。该方法的结果与0.4-0.7bpp之间的逐级类型一样好。当速率大于0.7bpp时，定向分组型上级其它类型，当速率小于0.4bpp时，全子带型上级其它类型。(3)重要样本类型：在水平扫描中，由n个幅值大于阈值的连续有效样本构成一个向量。然后，为了压缩这些重要样本的地址信息，我们对每个样本块而不是单个样本进行有效或无效的判断。该方案在编码性能方面上级上述方法。

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.