Perceptual quality metric with internal generative mechanism

IEEE Transactions on Image Processing

Volumn 22, Issue 1, 2013, Pages 43-54

  Wu, Jinjian ^a   Lin, Weisi ^b   Shi, Guangming ^a   Liu, Anmin ^b  

^a XIDIAN UNIVERSITY   (China)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Human visual system; image decomposition; image quality assessment (IQA); internal generative mechanism (IGM)

Indexed keywords

AUTO-REGRESSIVE; EVALUATION RESULTS; GENERATIVE MECHANISM; HUMAN PERCEPTION; HUMAN VISUAL SYSTEM; IMAGE DECOMPOSITION; IMAGE PERCEPTION; IMAGE QUALITY ASSESSMENT; NOISE ENERGY; OBJECTIVE IMAGE QUALITY ASSESSMENT; OVERALL QUALITY; PEAK SIGNAL-TO-NOISE RATIO; PERCEPTUAL QUALITY; PREDICTION ALGORITHMS; QUALITY METRICS; RESIDUAL CONTENT; SENSORY INFORMATION; STRUCTURAL SIMILARITY; UNCERTAIN INFORMATIONS; VISUAL CONTENT; VISUAL INFORMATION;

ALGORITHMS;

IMAGE QUALITY;

ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; FACTUAL DATABASE; HUMAN; IMAGE PROCESSING; METHODOLOGY; SIGNAL NOISE RATIO; THEORETICAL MODEL; VIDEORECORDING; VISION;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; DATABASES, FACTUAL; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MODELS, THEORETICAL; SIGNAL-TO-NOISE RATIO; VIDEO RECORDING; VISUAL PERCEPTION;

EID: 84871653541     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2012.2214048     Document Type: Article

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