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cs.MM2020★ 1 cited
Comment on "No-Reference Video Quality Assessment Based on the Temporal Pooling of Deep Features"
Franz Götz-Hahn, Vlad Hosu, Dietmar Saupe
In Neural Processing Letters 50,3 (2019) a machine learning approach to blind video quality assessment was proposed. It is based on temporal pooling of features of video frames, ta…
cs.MM2020★ 30 cited
SUR-FeatNet: Predicting the Satisfied User Ratio Curvefor Image Compression with Deep Feature Learning
Hanhe Lin, Vlad Hosu, Chunling Fan +4
The satisfied user ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the complementary cumulative distribution function of the just noticeable diffe…
cs.MM2019
KonVid-150k: A Dataset for No-Reference Video Quality Assessment of Videos in-the-Wild
Franz Götz-Hahn, Vlad Hosu, Hanhe Lin +1
Video quality assessment (VQA) methods focus on particular degradation types, usually artificially induced on a small set of reference videos. Hence, most traditional VQA methods u…