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pro vyhledávání: '"Mitra, Shankhanil"'
The design of no-reference (NR) image quality assessment (IQA) algorithms is extremely important to benchmark and calibrate user experiences in modern visual systems. A major drawback of state-of-the-art NR-IQA methods is their limited ability to gen
Externí odkaz:
http://arxiv.org/abs/2406.04654
Perceptual quality assessment of user generated content (UGC) videos is challenging due to the requirement of large scale human annotated videos for training. In this work, we address this challenge by first designing a self-supervised Spatio-Tempora
Externí odkaz:
http://arxiv.org/abs/2312.15425
No-reference (NR) image quality assessment (IQA) is an important tool in enhancing the user experience in diverse visual applications. A major drawback of state-of-the-art NR-IQA techniques is their reliance on a large number of human annotations to
Externí odkaz:
http://arxiv.org/abs/2312.04838
While the design of blind image quality assessment (IQA) algorithms has improved significantly, the distribution shift between the training and testing scenarios often leads to a poor performance of these methods at inference time. This motivates the
Externí odkaz:
http://arxiv.org/abs/2307.14735
Designing learning-based no-reference (NR) video quality assessment (VQA) algorithms for camera-captured videos is cumbersome due to the requirement of a large number of human annotations of quality. In this work, we propose a semi-supervised learnin
Externí odkaz:
http://arxiv.org/abs/2211.17075
Completely blind video quality assessment (VQA) refers to a class of quality assessment methods that do not use any reference videos, human opinion scores or training videos from the target database to learn a quality model. The design of this class
Externí odkaz:
http://arxiv.org/abs/2207.06148
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