Blind image quality assessment via deep learning

IEEE Transactions on Neural Networks and Learning Systems

Volumn 26, Issue 6, 2015, Pages 1275-1286

  Hou, Weilong ^a   Gao, Xinbo ^a   Tao, Dacheng ^b   Li, Xuelong ^c  

^a XIDIAN UNIVERSITY   (China)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^c XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

Author keywords

Deep learning; Image quality assessment (IQA); Natural scene statistics (NSS); No reference

Indexed keywords

BENCHMARKING; CLASSIFICATION (OF INFORMATION); COMPUTATIONAL LINGUISTICS; FACE RECOGNITION; IMAGE QUALITY; LINGUISTICS; MAPPING;

CLASSIFICATION FRAMEWORK; DEEP LEARNING; IMAGE QUALITY ASSESSMENT; IMAGE QUALITY ASSESSMENT (IQA); NATURAL SCENE STATISTICS; NO REFERENCES; OBJECTIVE IMAGE QUALITY ASSESSMENT; SMALL SAMPLE SIZE PROBLEMS;

QUALITY CONTROL;

ALGORITHM; AUTOMATED PATTERN RECOGNITION; BIOMIMETICS; COMPUTER ASSISTED DIAGNOSIS; FACTUAL DATABASE; HUMAN; IMAGE ENHANCEMENT; LEARNING; PROCEDURES; THEORETICAL MODEL; VISION;

ALGORITHMS; BIOMIMETICS; DATABASES, FACTUAL; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LEARNING; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED; VISUAL PERCEPTION;

EID: 84930197754     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2014.2336852     Document Type: Article

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