[1]罗志伟,冯桂.双策略高效视频编码帧间模式快速决策算法[J].华侨大学学报(自然科学版),2018,39(2):262-267.[doi:10.11830/ISSN.1000-5013.201603051]
　LUO Zhiwei,FENG Gui.Fast Decision Algorithm of Inter-Frame Mode Using Double Methods for High Efficient Video Coding[J].Journal of Huaqiao University(Natural Science),2018,39(2):262-267.[doi:10.11830/ISSN.1000-5013.201603051]
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双策略高效视频编码帧间模式快速决策算法()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第39卷
期数:
2018年第2期
页码:
262-267
栏目:
出版日期:
2018-03-20

文章信息/Info

Title:
Fast Decision Algorithm of Inter-Frame Mode Using Double Methods for High Efficient Video Coding
文章编号:
1000-5013(2018)02-0262-06
作者:
罗志伟;  冯桂
华侨大学 信息科学与工程学院, 福建 厦门 361021
Author(s):
LUO Zhiwei;  FENG Gui
College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
关键词:
高效视频编码;  快速选择;  当前块;  邻近块;  残差纹理
Keywords:
high efficient video coding;  fast decision;  current block;  neighboring block;  residual texture
分类号:
TN919.81
DOI:
10.11830/ISSN.1000-5013.201603051
文献标志码:
A
摘要:
为了减少编码复杂度,提出针对编码单元(CU)级和预测单元(PU)级的帧间快速算法.首先,利用当前编码块的3个空间邻近已编码块的深度信息,提前跳过某些CU的编码模式的检测.其次,利用当前编码块执行完Inter_2N×2N模式后,得出的残差块的纹理信息跳过某些PU的编码模式检测.实验结果表明:文中算法可以节省45.03%的编码时间;BD-BR(bjøntegaard delta bit rate)造成1.49%的损失;算法能够很大程度节省编码时间;引入的率失真性能损失忽略不计.
Abstract:
In order to reduce coding complexity, we proposed inter-frame fast decision algorithm for coding unit(CU)and prediction unit(PU). Firstly, using three spatially adjacent coding block depth information of the current coding block to skip certain CU encoding mode detection. Then, using the texture information of the residual block after performed the Inter_2N×2N mode to skip certain PU encoding mode detection. Experimental results show that the algorithm can save coding time 45.03%, while 1.49% of the loss of BD-BR(bjøntegaard delta bit rate). The algorithm proposed can also largely save coding time, and rate-distortion performance loss is negligible.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2016-03-16
通信作者: 冯桂(1960-),女,教授,博士,主要从事信号与信息处理、通信系统信息安全理论、多媒体通信、计算机视觉、模式识别与影像理解的研究.E-mail:fengg@hqu.edu.cn.
基金项目: 福建省自然科学基金资助项目(2016J01306)
更新日期/Last Update: 2018-03-20