Blind Stereo Quality Assessment Based on Learned Features From Binocular Combined Images

Quality assessment of stereo images confronts more challenges than its 2D counterparts. Direct use of 2D assessment methods is not sufficient to deal with the challenges of 3D perception. In this paper, an efficient general-purpose no-reference stereo image quality assessment, based on unsupervised...

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Bibliographic Details
Published in:IEEE transactions on multimedia 2017-11, Vol.19 (11), p.2475-2489
Main Authors: Karimi, Maryam, Nejati, Mansour, Reza Soroushmehr, S. M., Samavi, Shadrokh, Karimi, Nader, Najarian, Kayvan
Format: Article
Language:English
Subjects:
3D perception
binocular combination
Dictionaries
Estimation
Feature extraction
Image contrast
Image quality
no-reference (NR) image quality assessment (IQA)
Phase distortion
Quality assessment
Regression models
Space perception
sparse representation
stereo image quality assessment (SIQA)
Three-dimensional displays
Two dimensional displays
unsupervised feature learning
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Summary:Quality assessment of stereo images confronts more challenges than its 2D counterparts. Direct use of 2D assessment methods is not sufficient to deal with the challenges of 3D perception. In this paper, an efficient general-purpose no-reference stereo image quality assessment, based on unsupervised feature learning, is presented. The proposed method extracts features without any prior knowledge about the types and levels of distortions. This property enables our method to be adaptable for different applications. The perceived contrast and phase of the binocular combination of original stereo images are utilized to learn individual dictionaries. For each distorted stereo image, two feature vectors are pooled, in a hierarchical manner, over all sparse representation vectors of phase and contrast blocks by their corresponding dictionaries. Performance results of learning a regression model by the features acknowledge the superiority of the proposed method to state-of-the-art algorithms.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2017.2699082