{"title":"Fast motion estimation algorithm combining search point sampling technique with adaptive search range algorithm","authors":"Y. Ko, Hyun-Soo Kang, Jae-Won Suh","doi":"10.1109/MWSCAS.2012.6292188","DOIUrl":null,"url":null,"abstract":"This paper presents an enhanced fast motion estimation method where a search point sampling technique is combined with the adaptive search range algorithm (ASRA) based on the distribution of motion vector differences, which is our previous work. Since the ASRA is based on downsizing of search ranges for less computational complexity rather than sub-sampling of search points that is adopted by most of the fast algorithms, it results in smaller search areas where all points are considered as search points. Therefore, the conventional fast algorithms based on search point sampling techniques such as three-step search algorithm can be easily employed to the ASRA. As a result, we propose an algorithm where a part of the points within the search areas determined by the ASRA are sampled as the search points. Experimental results show that the proposed method reduces complexity of our ASRA by about 60% without quality degradation.","PeriodicalId":324891,"journal":{"name":"2012 IEEE 55th International Midwest Symposium on Circuits and Systems (MWSCAS)","volume":"185 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 55th International Midwest Symposium on Circuits and Systems (MWSCAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MWSCAS.2012.6292188","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
This paper presents an enhanced fast motion estimation method where a search point sampling technique is combined with the adaptive search range algorithm (ASRA) based on the distribution of motion vector differences, which is our previous work. Since the ASRA is based on downsizing of search ranges for less computational complexity rather than sub-sampling of search points that is adopted by most of the fast algorithms, it results in smaller search areas where all points are considered as search points. Therefore, the conventional fast algorithms based on search point sampling techniques such as three-step search algorithm can be easily employed to the ASRA. As a result, we propose an algorithm where a part of the points within the search areas determined by the ASRA are sampled as the search points. Experimental results show that the proposed method reduces complexity of our ASRA by about 60% without quality degradation.