No-Reference Image Quality Assessment
2 papers with code ·
Computer Vision
Subtask of
Image Quality Assessment
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RankIQA: Learning from Rankings for No-reference Image Quality Assessment
To address the problem of limited IQA dataset size, we train a Siamese Network to rank images in terms of image quality by using synthetically generated distortions for which relative image quality is known. Furthermore, on the LIVE benchmark we show that our approach is superior to existing NR-IQA techniques and that we even outperform the state-of-the-art in full-reference IQA (FR-IQA) methods without having to resort to high-quality reference images to infer IQA.
Robust statistics and no-reference image quality assessment in Curvelet domain
This paper uses robust statistics and curvelet transform to learn a general-purpose no-reference (NR) image quality assessment (IQA) model. The new approach, here called M1, competes with the Curvelet Quality Assessment proposed in 2014 (Curvelet2014).