{"title":"基于兴趣区域注意模型的HEVC复杂度控制","authors":"Xin Deng, Mai Xu, Shengxi Li, Zulin Wang","doi":"10.1109/VCIP.2014.7051545","DOIUrl":null,"url":null,"abstract":"In this paper, we present a novel complexity control method of HEVC to adjust its encoding complexity. First, a region-of-interest (ROI) attention model is established, which defines different weights for various regions according to their importance. Then, the complexity control algorithm is proposed with a distortion-complexity optimization model, to determine the maximum depth of the largest coding units (LCUs) according to their weights. We can reduce the encoding complexity to a given target level at the cost of little distortion loss. Finally, the experimental results show that the encoding complexity can drop to a pre-defined target complexity as low as 20% with bias less than 7%. Meanwhile, our method is verified to preserve the quality of ROI better than another state-of-the-art approach.","PeriodicalId":166978,"journal":{"name":"2014 IEEE Visual Communications and Image Processing Conference","volume":"418 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Complexity control of HEVC based on region-of-interest attention model\",\"authors\":\"Xin Deng, Mai Xu, Shengxi Li, Zulin Wang\",\"doi\":\"10.1109/VCIP.2014.7051545\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a novel complexity control method of HEVC to adjust its encoding complexity. First, a region-of-interest (ROI) attention model is established, which defines different weights for various regions according to their importance. Then, the complexity control algorithm is proposed with a distortion-complexity optimization model, to determine the maximum depth of the largest coding units (LCUs) according to their weights. We can reduce the encoding complexity to a given target level at the cost of little distortion loss. Finally, the experimental results show that the encoding complexity can drop to a pre-defined target complexity as low as 20% with bias less than 7%. Meanwhile, our method is verified to preserve the quality of ROI better than another state-of-the-art approach.\",\"PeriodicalId\":166978,\"journal\":{\"name\":\"2014 IEEE Visual Communications and Image Processing Conference\",\"volume\":\"418 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Visual Communications and Image Processing Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VCIP.2014.7051545\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Visual Communications and Image Processing Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VCIP.2014.7051545","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Complexity control of HEVC based on region-of-interest attention model
In this paper, we present a novel complexity control method of HEVC to adjust its encoding complexity. First, a region-of-interest (ROI) attention model is established, which defines different weights for various regions according to their importance. Then, the complexity control algorithm is proposed with a distortion-complexity optimization model, to determine the maximum depth of the largest coding units (LCUs) according to their weights. We can reduce the encoding complexity to a given target level at the cost of little distortion loss. Finally, the experimental results show that the encoding complexity can drop to a pre-defined target complexity as low as 20% with bias less than 7%. Meanwhile, our method is verified to preserve the quality of ROI better than another state-of-the-art approach.