Machine Learning Based Image Quality Assessment Model
Date Issued
2014
Date
2014
Author(s)
Chen, Li-Heng
Abstract
The objective image quality assessment (IQA) plays a key role in the development of various multimedia applications. The release of new IQA dataset (TID2013) challenges the wildly used 2D IQA metrics (e.g. PSNR and SSIM) since they cannot handle the diversity of distortion types. In this thesis, we propose a machine learning approach IQA model with features extracted from different frequency band (DOG features). The color distortion is also considered in our system. The effectiveness of our IQA system is verified by comparing with subjective score on the available databases. The experimental results show the high consistency between MOS score and our metric.
Subjects
人眼視覺系統
影像品質評估
結構相似性
支持向量機
Type
thesis
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