Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142241
Title: Toward domain transfer for no-reference quality prediction of asymmetrically distorted stereoscopic images
Authors: Shao, Feng
Zhang, Zhuqing
Jiang, Qiuping
Lin, Weisi
Jiang, Gangyi
Keywords: Engineering::Computer science and engineering
Issue Date: 2016
Source: Shao, F., Zhang, Z., Jiang, Q., Lin, W., & Jiang, G. (2018). Toward domain transfer for no-reference quality prediction of asymmetrically distorted stereoscopic images. IEEE Transactions on Circuits and Systems for Video Technology, 28(3), 573-585. doi:10.1109/TCSVT.2016.2628082
Journal: IEEE Transactions on Circuits and Systems for Video Technology
Abstract: We have presented a no-reference quality prediction method for asymmetrically distorted stereoscopic images, which aims to transfer the information from source feature domain to its target quality domain using a label consistent K-singular value decomposition classification framework. To this end, we construct a category-deviation database for dictionary learning that assigns a label for each stereoscopic image to indicate if it is noticeable or unnoticeable by human eyes. Then, by incorporating a category consistent term into the objective function, we learn view-specific feature and quality dictionaries to establish a semantic framework between the source feature domain and the target quality domain. The quality pooling is comparatively simple and only needs to estimate the quality score based on the classification probability. The experimental results demonstrate the effectiveness of our blind metric.
URI: https://hdl.handle.net/10356/142241
ISSN: 1051-8215
DOI: 10.1109/TCSVT.2016.2628082
Rights: © 2016 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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