690 citations · 1.6k across the 19 of their papers we have counts for
5 papers · 1 filter
Critical analysis on the reproducibility of visual quality assessment using deep features
Franz Götz-Hahn, Vlad Hosu, Dietmar Saupe
Data used to train supervised machine learning models are commonly split into independent training, validation, and test sets. This paper illustrates that complex data leakage case…
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…
DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning
Hanhe Lin, Vlad Hosu, Dietmar Saupe
Multi-level deep-features have been driving state-of-the-art methods for aesthetics and image quality assessment (IQA). However, most IQA benchmarks are comprised of artificially d…
Subjective Annotation for a Frame Interpolation Benchmark using Artefact Amplification
Hui Men, Vlad Hosu, Hanhe Lin +2
Current benchmarks for optical flow algorithms evaluate the estimation either directly by comparing the predicted flow fields with the ground truth or indirectly by using the predi…
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…