{"title":"基于VMAF的视频编码率失真优化","authors":"Sai Deng, Jingning Han, Yaowu Xu","doi":"10.1109/MMSP48831.2020.9287114","DOIUrl":null,"url":null,"abstract":"Video Multi-method Assessment Fusion (VMAF) is a machine-learning based video quality metric. It is experimentally shown to provide higher correlation with human visual system as compared to conventional metrics like peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) in many scenarios and has drawn considerable interest as an alternative metric to evaluate the perceptual quality. This work proposes a systematic approach to improve the video compression performance in VMAF. It is composed of multiple components including a pre-processing stage with a complement automatic filter parameter selection, and a modified rate-distortion optimization framework tailored for VMAF metric. The proposed scheme achieves on average 37% BD-rate reduction in VMAF, as compared to conventional video codec optimized for PSNR.","PeriodicalId":188283,"journal":{"name":"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"VMAF Based Rate-Distortion Optimization for Video Coding\",\"authors\":\"Sai Deng, Jingning Han, Yaowu Xu\",\"doi\":\"10.1109/MMSP48831.2020.9287114\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Video Multi-method Assessment Fusion (VMAF) is a machine-learning based video quality metric. It is experimentally shown to provide higher correlation with human visual system as compared to conventional metrics like peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) in many scenarios and has drawn considerable interest as an alternative metric to evaluate the perceptual quality. This work proposes a systematic approach to improve the video compression performance in VMAF. It is composed of multiple components including a pre-processing stage with a complement automatic filter parameter selection, and a modified rate-distortion optimization framework tailored for VMAF metric. The proposed scheme achieves on average 37% BD-rate reduction in VMAF, as compared to conventional video codec optimized for PSNR.\",\"PeriodicalId\":188283,\"journal\":{\"name\":\"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)\",\"volume\":\"88 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MMSP48831.2020.9287114\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMSP48831.2020.9287114","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
VMAF Based Rate-Distortion Optimization for Video Coding
Video Multi-method Assessment Fusion (VMAF) is a machine-learning based video quality metric. It is experimentally shown to provide higher correlation with human visual system as compared to conventional metrics like peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) in many scenarios and has drawn considerable interest as an alternative metric to evaluate the perceptual quality. This work proposes a systematic approach to improve the video compression performance in VMAF. It is composed of multiple components including a pre-processing stage with a complement automatic filter parameter selection, and a modified rate-distortion optimization framework tailored for VMAF metric. The proposed scheme achieves on average 37% BD-rate reduction in VMAF, as compared to conventional video codec optimized for PSNR.
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