Pan Jeng-Shyang, Lu Zhe-Ming, Sun Sheng-He
Dept. of Electron. Eng., Nat. Kaohsiung Univ. of Appl. Sci., Taiwan.
IEEE Trans Image Process. 2003;12(3):265-70. doi: 10.1109/TIP.2003.810587.
In this paper, a new and fast encoding algorithm for vector quantization is presented. This algorithm makes full use of two characteristics of a vector: the sum and the variance. A vector is separated into two subvectors: one is composed of the first half of vector components and the other consists of the remaining vector components. Three inequalities based on the sums and variances of a vector and its two subvectors components are introduced to reject those codewords that are impossible to be the nearest codeword, thereby saving a great deal of computational time, while introducing no extra distortion compared to the conventional full search algorithm. The simulation results show that the proposed algorithm is faster than the equal-average nearest neighbor search (ENNS), the improved ENNS, the equal-average equal-variance nearest neighbor search (EENNS) and the improved EENNS algorithms. Comparing with the improved EENNS algorithm, the proposed algorithm reduces the computational time and the number of distortion calculations by 2.4% to 6% and 20.5% to 26.8%, respectively. The average improvements of the computational time and the number of distortion calculations are 4% and 24.6% for the codebook sizes of 128 to 1024, respectively.
本文提出了一种新的快速矢量量化编码算法。该算法充分利用了矢量的两个特性：和与方差。一个矢量被分离为两个子矢量：一个由矢量分量的前半部分组成，另一个由其余的矢量分量组成。引入了基于矢量及其两个子矢量分量的和与方差的三个不等式，以剔除那些不可能是最近码字的码字，从而节省大量计算时间，同时与传统的全搜索算法相比不会引入额外的失真。仿真结果表明，所提出的算法比等均值最近邻搜索（ENNS）、改进的ENNS、等均值等方差最近邻搜索（EENNS）和改进的EENNS算法更快。与改进的EENNS算法相比，所提出的算法分别将计算时间和失真计算次数减少了2.4%至6%和20.5%至26.8%。对于码本大小从128到1024的情况，计算时间和失真计算次数的平均改进分别为4%和24.6%。
