{"title":"A Database and Model for the Visual Quality Assessment of Super-Resolution Videos","authors":"Fei Zhou;Wei Sheng;Zitao Lu;Guoping Qiu","doi":"10.1109/TBC.2024.3382949","DOIUrl":null,"url":null,"abstract":"Video super-resolution (SR) has important real world applications such as enhancing viewing experiences of legacy low-resolution videos on high resolution display devices. However, there are no visual quality assessment (VQA) models specifically designed for evaluating SR videos while such models are crucially important both for advancing video SR algorithms and for viewing quality assurance. This paper addresses this gap. We start by contributing the first video super-resolution quality assessment database (VSR-QAD) which contains 2,260 SR videos annotated with mean opinion score (MOS) labels collected through an approximately 400 man-hours psychovisual experiment by a total of 190 subjects. We then build on the new VSR-QAD and develop the first VQA model specifically designed for evaluating SR videos. The model features a two-stream convolutional neural network architecture and a two-stage training algorithm designed for extracting spatial and temporal features characterizing the quality of SR videos. We present experimental results and data analysis to demonstrate the high data quality of VSR-QAD and the effectiveness of the new VQA model for measuring the visual quality of SR videos. The new database and the code of the proposed model will be available online at \n<uri>https://github.com/key1cdc/VSRQAD</uri>\n.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 2","pages":"516-532"},"PeriodicalIF":3.2000,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Broadcasting","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10497116/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Video super-resolution (SR) has important real world applications such as enhancing viewing experiences of legacy low-resolution videos on high resolution display devices. However, there are no visual quality assessment (VQA) models specifically designed for evaluating SR videos while such models are crucially important both for advancing video SR algorithms and for viewing quality assurance. This paper addresses this gap. We start by contributing the first video super-resolution quality assessment database (VSR-QAD) which contains 2,260 SR videos annotated with mean opinion score (MOS) labels collected through an approximately 400 man-hours psychovisual experiment by a total of 190 subjects. We then build on the new VSR-QAD and develop the first VQA model specifically designed for evaluating SR videos. The model features a two-stream convolutional neural network architecture and a two-stage training algorithm designed for extracting spatial and temporal features characterizing the quality of SR videos. We present experimental results and data analysis to demonstrate the high data quality of VSR-QAD and the effectiveness of the new VQA model for measuring the visual quality of SR videos. The new database and the code of the proposed model will be available online at
https://github.com/key1cdc/VSRQAD
.
期刊介绍:
The Society’s Field of Interest is “Devices, equipment, techniques and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.” In addition to this formal FOI statement, which is used to provide guidance to the Publications Committee in the selection of content, the AdCom has further resolved that “broadcast systems includes all aspects of transmission, propagation, and reception.”