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RankIQA: Learning from Rankings for No-reference Image Quality Assessment
[article]
2017
arXiv
pre-print
We propose a no-reference image quality assessment (NR-IQA) approach that learns from rankings (RankIQA). 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. These ranked image sets can be automatically generated without laborious human labeling. We then use fine-tuning to transfer the knowledge represented in the trained Siamese Network to
arXiv:1707.08347v1
fatcat:vm62nereqvhqzl3wfxydmkz6ci