{"title":"Advanced video debanding","authors":"G. Baugh, A. Kokaram, François Pitié","doi":"10.1145/2668904.2668912","DOIUrl":null,"url":null,"abstract":"High efficiency video coding has made it possible to stream video over bandwidth constrained communication networks. Depending on bit rate requirements, a video encoder sacrifices some image details which can then introduce visual artefacts. Due to aggressive encoding a contouring staircase artefact called banding can be observed in image regions with very low texture. This paper presents a solution for removing banding artefacts using image filtering and dithering techniques. A new banding index (BI) metric is also presented for quantitatively measuring the amount of banding in an image. Using this BI metric, we assess how much banding YouTube video encoding introduces in a video test dataset. There is a debanding filter in ffmpeg called gradfun. We compare the results of our debanding technique with those of gradfun on the YouTube test dataset.","PeriodicalId":401915,"journal":{"name":"Proceedings of the 11th European Conference on Visual Media Production","volume":"268 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 11th European Conference on Visual Media Production","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2668904.2668912","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16
Abstract
High efficiency video coding has made it possible to stream video over bandwidth constrained communication networks. Depending on bit rate requirements, a video encoder sacrifices some image details which can then introduce visual artefacts. Due to aggressive encoding a contouring staircase artefact called banding can be observed in image regions with very low texture. This paper presents a solution for removing banding artefacts using image filtering and dithering techniques. A new banding index (BI) metric is also presented for quantitatively measuring the amount of banding in an image. Using this BI metric, we assess how much banding YouTube video encoding introduces in a video test dataset. There is a debanding filter in ffmpeg called gradfun. We compare the results of our debanding technique with those of gradfun on the YouTube test dataset.