Blind Image Quality Assessment
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A Probabilistic Quality Representation Approach to Deep Blind Image Quality Prediction
Recognizing this, we propose a new representation of perceptual image quality, called probabilistic quality representation (PQR), to describe the image subjective score distribution, whereby a more robust loss function can be employed to train a deep BIQA model.
Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images
• lidq92/SFA
• 
To guarantee a satisfying Quality of Experience (QoE) for consumers, it is required to measure image quality efficiently and reliably.
BLIND IMAGE QUALITY ASSESSMENT IMAGE QUALITY ESTIMATION NO-REFERENCE IMAGE QUALITY ASSESSMENT
Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?
IEEE Transactions on Multimedia 2018
• lidq92/SFA
•
The proposed method, SFA, is compared with nine representative blur-specific NR-IQA methods, two general-purpose NR-IQA methods, and two extra full-reference IQA methods on Gaussian blur images (with and without Gaussian noise/JPEG compression) and realistic blur images from multiple databases, including LIVE, TID2008, TID2013, MLIVE1, MLIVE2, BID, and CLIVE.
BLIND IMAGE QUALITY ASSESSMENT IMAGE CLASSIFICATION IMAGE QUALITY ESTIMATION NO-REFERENCE IMAGE QUALITY ASSESSMENT