Media Lab., MIT, Cambridge, MA.
IEEE Trans Image Process. 1992;1(4):526-33. doi: 10.1109/83.199923.
Vector quantization for entropy coding of image subbands is investigated. Rate distortion curves are computed with mean square error as a distortion criterion. The authors show that full-search entropy-constrained vector quantization of image subbands results in the best performance, but is computationally expensive. Lattice quantizers yield a coding efficiency almost indistinguishable from optimum full-search entropy-constrained vector quantization. Orthogonal lattice quantizers were found to perform almost as well as lattice quantizers derived from dense sphere packings. An optimum bit allocation rule based on a Lagrange multiplier formulation is applied to subband coding. Coding results are shown for a still image.
本文研究了图像子带熵编码的矢量量化。以均方误差作为失真准则计算了率失真曲线。作者表明，图像子带的完全搜索熵约束矢量量化可获得最佳性能，但计算量较大。格型量化器的编码效率几乎与最佳的完全搜索熵约束矢量量化无法区分。正交格型量化器的性能几乎与基于密集球堆积的格型量化器一样好。应用基于拉格朗日乘子公式的最优比特分配规则于子带编码。给出了一幅静止图像的编码结果。
