Blind Image Quality Assessment Using Local Consistency Aware Retriever and Uncertainty Aware Evaluator

Blind image quality assessment (BIQA) aims to automatically predict the perceptual quality of a digital image without accessing its pristine reference. Previous studies mainly focus on extracting various quality-relevant image features. By contrast, the explorations on highly efficient learning mode...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2018-09, Vol.28 (9), p.2078-2089
Main Authors: Wu, Qingbo, Li, Hongliang, Ngan, King N., Ma, Kede
Format: Article
Language:English
Subjects:
Blind image quality assessment
Consistency
Digital imaging
Distortion
Feature extraction
Gaussian process
Image contrast
Image processing
Image quality
Integrated circuit modeling
Learning
local consistency aware retriever
Mathematical models
Predictions
Predictive models
Probabilistic logic
Quality
Quality assessment
Training
Uncertainty
uncertainty aware evaluator
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Summary:Blind image quality assessment (BIQA) aims to automatically predict the perceptual quality of a digital image without accessing its pristine reference. Previous studies mainly focus on extracting various quality-relevant image features. By contrast, the explorations on highly efficient learning model are still very limited. Motivated by the fact that it is difficult to approximate a complex and large data set via a global parametric model, we propose a novel local learning method for BIQA to improve quality prediction performance. More specifically, we search for the perceptually similar neighbors of a test image to serve as its unique training set. Unlike the widely used {k} nearest neighbors principle, which only measures the similarity between the testing and training samples, the local consistency of the selected training data is also considered to generate smoother sample space. The image quality is estimated via a sparse Gaussian process. As an additional benefit, the uncertainty of the predicted score is jointly inferred, which can subsequently drive more robust perceptual image processing applications, such as deblocking investigated in this paper. Extensive experiments demonstrate that the proposed learning model leads to consistent quality prediction improvements over many state-of-the-art BIQA algorithms.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2017.2710419