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Found 6 papers, 2 papers with code
Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?
Perceptual losses play an important role in constructing deep-neural-network-based methods by increasing the naturalness and realism of processed images and videos.
Compressed Video Quality Assessment for Super-Resolution: a Benchmark and a Quality Metric
We assessed 17 state-ofthe-art SR models using our benchmark and evaluated their ability to preserve scene context and their susceptibility to compression artifacts.
Combining Contrastive and Supervised Learning for Video Super-Resolution Detection
To explain how the method detects videos, we systematically review the major components of our framework - in particular, we show that most data-augmentation approaches hinder the learning of the method.
Temporally Coherent Person Matting Trained on Fake-Motion Dataset
We propose a novel neural-network-based method to perform matting of videos depicting people that does not require additional user input such as trimaps.
Perceptually Motivated Method for Image Inpainting Comparison
The field of automatic image inpainting has progressed rapidly in recent years, but no one has yet proposed a standard method of evaluating algorithms.
Shot-by-Shot Movie Version Comparison
Some movies are released in two or more versions.
