{"title":"Medium-granularity computational complexity control for H.264/AVC","authors":"Xiang Li, M. Wien, J. Ohm","doi":"10.1109/PCS.2010.5702467","DOIUrl":null,"url":null,"abstract":"Today, video applications on handheld devices become more and more popular. Due to limited computational capability of handheld devices, complexity constrained video coding draws much attention. In this paper, a medium-granularity computational complexity control (MGCC) is proposed for H.264/AVC. First, a large dynamic range in complexity is achieved by taking 16×16 motion estimation in a single reference frame as the basic computational unit. Then a high coding efficiency is obtained by an adaptive computation allocation at MB level. Simulations show that coarse-granularity methods cannot work when the normalized complexity is below 15%. In contrast, the proposed MGCC performs well even when the complexity is reduced to 8.8%. Moreover, an average gain of 0.3 dB over coarse-granularity methods in BD-PSNR is obtained for 11 sequences when the complexity is around 20%.","PeriodicalId":255142,"journal":{"name":"28th Picture Coding Symposium","volume":"229 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"28th Picture Coding Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PCS.2010.5702467","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Today, video applications on handheld devices become more and more popular. Due to limited computational capability of handheld devices, complexity constrained video coding draws much attention. In this paper, a medium-granularity computational complexity control (MGCC) is proposed for H.264/AVC. First, a large dynamic range in complexity is achieved by taking 16×16 motion estimation in a single reference frame as the basic computational unit. Then a high coding efficiency is obtained by an adaptive computation allocation at MB level. Simulations show that coarse-granularity methods cannot work when the normalized complexity is below 15%. In contrast, the proposed MGCC performs well even when the complexity is reduced to 8.8%. Moreover, an average gain of 0.3 dB over coarse-granularity methods in BD-PSNR is obtained for 11 sequences when the complexity is around 20%.
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