Performance analysis of block matching motion estimation algorithms for HD videos with different search parameters

2016 International Conference on Intelligent Systems Engineering (ICISE) Pub Date : 1900-01-01 DOI:10.1109/INTELSE.2016.7475139

M. Muzammil, Zeshan Aslam Khan, I. Ali, M. O. Ullah

{"title":"Performance analysis of block matching motion estimation algorithms for HD videos with different search parameters","authors":"M. Muzammil, Zeshan Aslam Khan, I. Ali, M. O. Ullah","doi":"10.1109/INTELSE.2016.7475139","DOIUrl":null,"url":null,"abstract":"High Definition (HD) videos are the most widely used in HD television and mobile phones now a days for transmission and storage. Due to large data size, HD videos require efficient and robust video coding mechanism to enable real-time encoding. Numerous Motion Estimation (ME) algorithms are proposed to reduce the computational complexity of the coding process. In this paper, we present the performance analysis of some famous Block Matching ME Algorithms (BMAs) for HD videos. Different performance measuring parameters are used to evaluate the performance of BMAs, like Peak Signal to Noise Ratio (PSNR), ME time, Mean Square Error (MSE). The simulation results show that the Adaptive Rood Pattern Search (ARPS) ME algorithm outperforms in term of MSE, PSNR and number of search points, for HD (720p) videos, over various search parameters. ARPS, Diamond Search (DS) and Flatted Hexagon Search (FHS) ME algorithms improve the PSNR from 32dB to 48dB for some video sequences, by increasing search range, whereas the number of search points also increased with the same parameter that causes to increased ME time and computational complexity.","PeriodicalId":127671,"journal":{"name":"2016 International Conference on Intelligent Systems Engineering (ICISE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Intelligent Systems Engineering (ICISE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INTELSE.2016.7475139","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 3

Abstract

High Definition (HD) videos are the most widely used in HD television and mobile phones now a days for transmission and storage. Due to large data size, HD videos require efficient and robust video coding mechanism to enable real-time encoding. Numerous Motion Estimation (ME) algorithms are proposed to reduce the computational complexity of the coding process. In this paper, we present the performance analysis of some famous Block Matching ME Algorithms (BMAs) for HD videos. Different performance measuring parameters are used to evaluate the performance of BMAs, like Peak Signal to Noise Ratio (PSNR), ME time, Mean Square Error (MSE). The simulation results show that the Adaptive Rood Pattern Search (ARPS) ME algorithm outperforms in term of MSE, PSNR and number of search points, for HD (720p) videos, over various search parameters. ARPS, Diamond Search (DS) and Flatted Hexagon Search (FHS) ME algorithms improve the PSNR from 32dB to 48dB for some video sequences, by increasing search range, whereas the number of search points also increased with the same parameter that causes to increased ME time and computational complexity.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

不同搜索参数下高清视频块匹配运动估计算法性能分析

高清晰度(HD)视频在高清电视和移动电话中得到了最广泛的应用，用于传输和存储。由于数据量大，高清视频需要高效、鲁棒的视频编码机制来实现实时编码。为了降低编码过程的计算复杂度，提出了多种运动估计算法。在本文中，我们对一些著名的块匹配ME算法(BMAs)进行了性能分析。使用不同的性能测量参数来评估bma的性能，如峰值信噪比(PSNR)， ME时间，均方误差(MSE)。仿真结果表明，对于高清(720p)视频，在各种搜索参数下，自适应道路模式搜索(ARPS) ME算法在MSE、PSNR和搜索点数量方面都表现优异。ARPS、Diamond Search (DS)和Flatted Hexagon Search (FHS) ME算法通过增加搜索范围，将部分视频序列的PSNR从32dB提高到48dB，但在相同的参数下，搜索点数量也会增加，导致ME时间和计算复杂度增加。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

2016 International Conference on Intelligent Systems Engineering (ICISE)

自引率

0.00%

发文量