L. Bianchi, Riccardo Gatti, L. Lombardi, L. Cinque
{"title":"Parallel Lossy Compression for HD Images - A New Fast Image Magnification Algorithm for Lossy HD Video Decompression Over Commodity GPU","authors":"L. Bianchi, Riccardo Gatti, L. Lombardi, L. Cinque","doi":"10.5220/0001767900160021","DOIUrl":null,"url":null,"abstract":"Today High Definition (HD) for video contents is one of the biggest challenges in computer vision. The 1080i standard defines the minimum image resolution required to be classified as HD mode. At the same time bandwidth constraints and latency don’t allow the transmission of uncompressed, high resolution images. Often lossy compression algorithms are involved in the process of providing HD video streams, because of their high compression rate capabilities. The main issue concerned to these methods, while processing frames, is that high frequencies components in the image are neither conserved nor reconstructed. Our approach uses a simple downsampling algorithm for compression, but a new, very accurate method for decompression which is capable of high frequencies restoration. Our solution Is also highly parallelizable and can be efficiently implemented on a commodity parallel computing architecture, such as GPU, obtaining extremely fast performances.","PeriodicalId":411140,"journal":{"name":"International Conference on Computer Vision Theory and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Computer Vision Theory and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0001767900160021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Today High Definition (HD) for video contents is one of the biggest challenges in computer vision. The 1080i standard defines the minimum image resolution required to be classified as HD mode. At the same time bandwidth constraints and latency don’t allow the transmission of uncompressed, high resolution images. Often lossy compression algorithms are involved in the process of providing HD video streams, because of their high compression rate capabilities. The main issue concerned to these methods, while processing frames, is that high frequencies components in the image are neither conserved nor reconstructed. Our approach uses a simple downsampling algorithm for compression, but a new, very accurate method for decompression which is capable of high frequencies restoration. Our solution Is also highly parallelizable and can be efficiently implemented on a commodity parallel computing architecture, such as GPU, obtaining extremely fast performances.