[1]李娜娜,黄琨强,张秋闻,等.基于自适应量化器选择的编码率失真优化判决算法[J].郑州大学学报(工学版),2019,40(03):19-25.[doi:10.13705/j.issn.1671-6833.2019.03.001]
 Li Nana,Huang Kunqiang,Zhang Qiuwen,et al.Rate Distortion Optimization Decision Algorithm of Coding Based on Adaptive Quantizer Selection[J].Journal of Zhengzhou University (Engineering Science),2019,40(03):19-25.[doi:10.13705/j.issn.1671-6833.2019.03.001]
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基于自适应量化器选择的编码率失真优化判决算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
40
期数:
2019年03期
页码:
19-25
栏目:
出版日期:
2019-04-30

文章信息/Info

Title:
Rate Distortion Optimization Decision Algorithm of Coding Based on Adaptive Quantizer Selection
作者:
李娜娜1黄琨强2张秋闻1刘宽1
1. 郑州轻工业学院计算机与通信工程学院;2. 郑州工商学院机械与电信工程学院
Author(s):
Li Nana 1Huang Kunqiang 2Zhang Qiuwen 1Liu Kuan 1
1. School of Computer and Communication Engineering, Zhengzhou Institute of Light Industry; 2. School of Mechanical and Telecommunications Engineering, Zhengzhou Institute of Technology
关键词:
HEVC量化率失真复杂度
Keywords:
HEVCQuantifyrate distortionthe complexity
DOI:
10.13705/j.issn.1671-6833.2019.03.001
文献标志码:
A
摘要:
本文针对HEVC编码复杂度高的问题,提出一种高效的自适应量化器选择的率失真优化量化判决算法.通过对变换系数量化水平是否相等以及量化水平和是否小于设定阈值进行量化器判决,若量化水平相等或为零则直接采用传统RDOQ,否则进行阈值判决,从而最终确定最佳量化器.提出的算法能有效地对计算量小的均匀量化和比特率小的率失真优化量化这两种量化方法自适应选择,降低了编码器计算复杂度.实验结果表明,采用了该算法节省了大约10.52%的编码时间.
Abstract:
In order to solve the problem of high complexity of HEVC coding, an efficient adaptive quantizer selection and rate distortion optimized quantization decision algorithm is proposed in this paper. The quantizer determines whether the quantization level of the transform coefficient is equal or zero and whether the sum of quantization levels is less than the set threshold.If the quantization level is equal or zero, the traditional RDOQ is used directly.Otherwise, a threshold decision is made,and finally determine the optimal quantizer.The proposed decision algorithm can select two quantization methods: Uniform Scalar Quantizer with small computational complexity and RDOQ with low bit rate self-adaptively, and the computational complexity of the encoder is reduced.The experimental results shows that our proposed algorithm can save about 10.52% of coding time.
更新日期/Last Update: 2019-04-16