Learning to Rank for Blind Image Quality Assessment

Blind image quality assessment (BIQA) aims to predict perceptual image quality scores without access to reference images. State-of-the-art BIQA methods typically require subjects to score a large number of images to train a robust model. However, subjective quality scores are imprecise, biased, and...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 26; číslo 10; s. 2275 - 2290
Hlavní autori:	Gao, Fei, Tao, Dacheng, Gao, Xinbo, Li, Xuelong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.10.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Assessments Blinds Customer satisfaction Feature extraction Image coding Image quality Image quality assessment (IQA) Learning learning preferences learning to rank Mathematical models multiple kernel learning (MKL) Noise Observers Quality of service Training Transform coding universal blind IQA (BIQA) Image quality assessment (IQA) learning preferences universal blind IQA (BIQA) learning to rank multiple kernel learning (MKL)
ISSN:	2162-237X, 2162-2388, 2162-2388
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