Analysis of Public Image and Video Databases for Quality Assessment

By Stefan Winkler. IEEE Journal on Selected Topics in Signal Processing, 2012

309 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9c88c02a6c09ac8944b1d3d0c8bf0ef0641ce286

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In Computer Vision and Pattern Recognition

1. A. Bovik
2. Damon M. Chandler
3. Lei Zhang
4. C.-C. Jay Kuo
5. Jari Korhonen
6. Dacheng Tao
7. Zhou Wang
8. Weisi Lin

Citations in the corpus

• Perceptual Video Coding Based on SSIM-Inspired Divisive Normalization (2013, methodology): “This method is widely used in psychophysical studies [58], [59], where in each trial, a subject is shown a pair of video sequences and is asked (forced) to choose the one he/she thinks to have better quality.”
• UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content (2020, methodology): “As a way of characterizing the content diversity of the videos in each database, Winkler [16] suggested three quanti-”
• The Konstanz natural video database (KoNViD-1k) (2017, background): “Winkler [2] proposed several criteria for quantitative comparisons of source content, test conditions, and subjective ratings, applying them to 27 image and video databases.”
• Mean opinion score (MOS) revisited: methods and applications, limitations and alternatives (2016, background): “Large numbers of subjective MOS data [94] and objective quality metrics [37–39] have been made available.”
• Seven Challenges in Image Quality Assessment: Past, Present, and Future Research (2013, background): “In addition, in [120], Winkler has provided quantitative comparisons of various aspects (source content, test conditions, and subjective ratings) of some of these databases.”
• Image database TID 2013 : Peculiarities , results and perspectives (2016, methodology): “To this aim, it has been extensively used [18] [19] [20] [21] [22].”
• Two-Level Approach for No-Reference Consumer Video Quality Assessment (2019, methodology): “on compression artifacts in digital television, was published in 2000 [22].”
• Color image database TID2013: Peculiarities and preliminary results (2013, methodology): “To characterize adequateness and performance of these metrics, several databases of color images with certain types and levels of distortions have been intensively used [4] including the databases LIVE, Toyama, TID2008 [5], etc.”
• YouTube UGC Dataset for Video Compression Research (2019, unknown): “Thus, besides the three common attributes (spatial, temporal, and color) suggested in [17], we propose the variation of complexity across the video as the fourth attribute that reflects inter-chunk quality consistency.”