{"title":"基于ROI的视频编码多目标粒子群优化","authors":"Guangjie Ren, Feiyang Liu, Daiqin Yang, Yiyong Zha, Yunfei Zhang, Xin Liu","doi":"10.1145/3338533.3366608","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a new algorithm for High Efficiency Video Coding(HEVC) based on multi-objective particle swarm optimization (MOPSO) to enhance the visual quality of ROI while ensuring a certain overall quality. According to the R-λ model of detected ROI, the fitness function in MOPSO can be designed as the distortion of ROI and that of the overall frame. The particle consists of ROI's rate and other region's rate. After iterating through the multi-objective particle swarm optimization algorithm, the Pareto front is obtained. Then, the final bit allocation result which are the appropriate bit rate for ROI and non-ROI is selected from this set. Finally, according to the R-λ model, the coding parameters could be determined for coding. The experimental results show that the proposed algorithm improves the visual quality of ROI while guarantees overall visual quality.","PeriodicalId":273086,"journal":{"name":"Proceedings of the ACM Multimedia Asia","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Multi-Objective Particle Swarm Optimization for ROI based Video Coding\",\"authors\":\"Guangjie Ren, Feiyang Liu, Daiqin Yang, Yiyong Zha, Yunfei Zhang, Xin Liu\",\"doi\":\"10.1145/3338533.3366608\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a new algorithm for High Efficiency Video Coding(HEVC) based on multi-objective particle swarm optimization (MOPSO) to enhance the visual quality of ROI while ensuring a certain overall quality. According to the R-λ model of detected ROI, the fitness function in MOPSO can be designed as the distortion of ROI and that of the overall frame. The particle consists of ROI's rate and other region's rate. After iterating through the multi-objective particle swarm optimization algorithm, the Pareto front is obtained. Then, the final bit allocation result which are the appropriate bit rate for ROI and non-ROI is selected from this set. Finally, according to the R-λ model, the coding parameters could be determined for coding. The experimental results show that the proposed algorithm improves the visual quality of ROI while guarantees overall visual quality.\",\"PeriodicalId\":273086,\"journal\":{\"name\":\"Proceedings of the ACM Multimedia Asia\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ACM Multimedia Asia\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3338533.3366608\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ACM Multimedia Asia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3338533.3366608","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-Objective Particle Swarm Optimization for ROI based Video Coding
In this paper, we propose a new algorithm for High Efficiency Video Coding(HEVC) based on multi-objective particle swarm optimization (MOPSO) to enhance the visual quality of ROI while ensuring a certain overall quality. According to the R-λ model of detected ROI, the fitness function in MOPSO can be designed as the distortion of ROI and that of the overall frame. The particle consists of ROI's rate and other region's rate. After iterating through the multi-objective particle swarm optimization algorithm, the Pareto front is obtained. Then, the final bit allocation result which are the appropriate bit rate for ROI and non-ROI is selected from this set. Finally, according to the R-λ model, the coding parameters could be determined for coding. The experimental results show that the proposed algorithm improves the visual quality of ROI while guarantees overall visual quality.
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