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|標題:||A ParaBoost Method to Image Quality Assessment||作者:||劉宗榮
Joe Yuchieh Lin
C.-C. Jay Kuo
|關鍵字:||Auxiliary image quality scorer (AIQS);basic image quality scorer (BIQS);ensemble;parallel boosting (ParaBoost);scorer||出版社:||IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS||Project:||IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. 28, Page(s) 107-121||摘要:||
An ensemble method for full-reference image quality assessment (IQA) based on the parallel boosting (ParaBoost) idea is proposed in this paper. We first extract features from existing image quality metrics and train them to form basic image quality scorers (BIQSs). Then, we select additional features to address specific distortion types and train them to construct auxiliary image quality scorers (AIQSs). Both BIQSs and AIQSs are trained on small image subsets of certain distortion types and, as a result, they are weak performers with respect to a wide variety of distortions. Finally, we adopt the ParaBoost framework, which is a statistical scorer selection scheme for support vector regression (SVR), to fuse the scores of BIQSs and AIQSs to evaluate the images containing a wide range of distortion types. This ParaBoost methodology can be easily extended to images of new distortion types. Extensive experiments are conducted to demonstrate the superior performance of the ParaBoost method, which outperforms existing IQA methods by a significant margin. Specifically, the Spearman rank order correlation coefficients (SROCCs) of the ParaBoost method with respect to the LIVE, CSIQ, TID2008, and TID2013 image quality databases are 0.98, 0.97, 0.98, and 0.96, respectively.
|Appears in Collections:||通訊工程研究所|
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