###
Journal of Software:2020.31(11):3603-3620

采用自适应缩放系数优化的块匹配运动估计
宋传鸣,闫小红,葛明博,王相海,尹宝才
(辽宁师范大学 计算机与信息技术学院, 辽宁 大连 116029;大连理工大学 计算机科学与技术学院, 辽宁 大连 116024;计算机软件新技术国家重点实验室(南京大学), 江苏 南京 210023)
Optimized Block-matching Motion Estimation Using Adaptive Zoom Coefficient
SONG Chuan-Ming,YAN Xiao-Hong,GE Ming-Bo,WANG Xiang-Hai,YIN Bao-Cai
(School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China;School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China;State Key Laboratory for Novel Software Technology(Nanjing University), Nanjing 210023, China)
Abstract
Chart / table
Reference
Similar Articles
Article :Browse 54   Download 54
Received:October 27, 2018    Revised:February 25, 2019
> 中文摘要: 尽管基于平移模型的快速块匹配运动估计算法在一定程度上解决了高计算量的问题,但却是以牺牲运动补偿质量为代价的,而高阶运动模型尚存在计算量高、收敛不稳定的不足.通过实验统计发现,视频中约有56.21%的块包含缩放运动,进而得出缩放运动是除平移运动外最主要的视频运动形式的结论.进而借助双线性插值,在传统的块平移模型中引进一个缩放系数,将运动补偿误差表示为该缩放系数的一元二次函数,利用韦达定理推导出1D缩放运动下最佳缩放系数的计算方法,并将其进一步推广到2D等比例缩放运动的情况下.在此基础上,提出了一种采用自适应缩放系数优化的快速块匹配运动估计算法.该算法以菱形搜索计算平移矢量,再用自适应缩放系数确定待预测块的最佳匹配块.在33个标准测试视频上的实验结果表明,与基于平移模型的块匹配全搜索和快速菱形搜索相比,该算法的平均运动补偿峰值信噪比(peak signal-to-noise ratio,简称PSNR)分别提高了0.11dB和0.64dB,计算量比全搜索下降了96.02%,略高于菱形搜索;与基于缩放模型的运动估计相比,该算法的平均峰值信噪比较之3D全搜索下降了0.62dB,但是比快速3D菱形搜索提高了0.008dB,而计算量仅分别为两者的0.11%和3.86%,并且无需向解码端传输缩放矢量,能够实现编、解码端的自同步,不会增加边信息的码流开销.此外,该自适应缩放系数计算方法还可与菱形搜索以外的其他快速块匹配运动估计相结合,提高其运动补偿质量.
Abstract:Fast block-wise motion estimation algorithm based on translational model solves the high computational complexity issue to some extent, but it sacrifices the motion compensation quality, whilst the higher-order motion model still exhibits the problems of computationally inefficiency and unstable convergence. Through a number of experiments, it is found that about 56.21% of the video blocks contain zoom motion, thus a conclusion is drawn that zoom motion is one of the most important motion forms in video except for the translational motion. Therefore, a zoom coefficient is introduced into the conventional block-wise translational model by bilinear interpolation, and model the motion-compensated error into a quadratic function with regard to the zoom coefficient. Subsequently, the approach is derived to compute the optimal zoom coefficient under the condition of 1D zoom motion through Vieta’s theorem, which is further extended to the condition of 2D zoom motion with equal proportion. Based on the above, a fast block-matching motion estimation algorithm is presented and is optimized by the adaptive zoom coefficient. It first uses the diamond search (DS) to compute the translational motion vector, and then determines an optimal matching block for the block to be predicted with the adaptive zoom coefficient. Experimental results carried out on 33 standard test video sequences showed that the proposed algorithm gains separately 0.11 dB and 0.64 dB higher motion-compensated peak signal-to-noise ratio (PSNR) than those of the full search (FS) and the DS based on block-wise translational model. And its computational complexity is 96.02% lower than that of the FS, slightly higher than that of the DS. Compared with the motion estimation based on the zoom model, the average PSNR of the proposed algorithm is 0.62 dB lower than that of 3D full search, but 0.008 dB higher than that of fast 3D diamond search. And the computational complexity only amounts to 0.11% and 3.86% of the 3D full search and the 3D diamond search, respectively. Meanwhile, the proposed algorithm can realize the self- synchronization between the encoder and decoder without transmitting the zoom vectors, so it does not increase the overhead of the side information. Additionally, the proposed adaptive zoom coefficient computation can also be combined with state-of-art fast block-wise motion estimation algorithms other than the diamond search, improving their motion-compensation quality.
文章编号:     中图分类号:TP391    文献标志码:
基金项目:国家自然科学基金(61402214,41671439,61632006);辽宁省自然科学基金(20180550570);大连市青年科技之星支持计划(2015R069);计算机软件新技术国家重点实验室(南京大学)开放课题(KFKT2018B07);辽宁省高等学校创新团队支持计划(LT2017013);辽宁省高等学校创新人才支持计划([2018]478-64) 国家自然科学基金(61402214,41671439,61632006);辽宁省自然科学基金(20180550570);大连市青年科技之星支持计划(2015R069);计算机软件新技术国家重点实验室(南京大学)开放课题(KFKT2018B07);辽宁省高等学校创新团队支持计划(LT2017013);辽宁省高等学校创新人才支持计划([2018]478-64)
Foundation items:National Natural Science Foundation of China (61402214, 41671439, 61632006); Natural Science Foundation of Liaoning Province (20180550570); Program for Youth Science and Technology Star of Dalian City (2015R069); Open Foundation of State Key Laboratory for Novel Software Technology (Nanjing University) (KFKT2018B07); Program for Liaoning Innovative Research Team in University (LT2017013); Program for Liaoning Excellent Talents in University ([2018]478-64)
Reference text:

宋传鸣,闫小红,葛明博,王相海,尹宝才.采用自适应缩放系数优化的块匹配运动估计.软件学报,2020,31(11):3603-3620

SONG Chuan-Ming,YAN Xiao-Hong,GE Ming-Bo,WANG Xiang-Hai,YIN Bao-Cai.Optimized Block-matching Motion Estimation Using Adaptive Zoom Coefficient.Journal of Software,2020,31(11):3603-3620