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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Bibliographic Details
Published in:	IEEE transaction on neural networks and learning systems Vol. 26; no. 10; pp. 2275 - 2290
Main Authors:	Gao, Fei, Tao, Dacheng, Gao, Xinbo, Li, Xuelong
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.10.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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