No-Reference Learning-Based and Human Visual-Based Image Quality Assessment Metric

With the rapid growth of multimedia applications and technologies, objective image quality assessment (IQA) became a topic of fundamental interest. No-Reference (NR) IQA algorithms are more suitable to real-world applications where the original image is not available. In order to be more consistent with human perception, this paper proposes a new NR-IQA metric where the input image is firstly decomposed to several frequency sub-bands which mimic the human visual system (HVS). Then, the statistical features are extracted from these frequency bands and used to fit a multivariate Gaussian distribution (MVGD). Finally, the model obtained by training predicts the quality of the input image. Experimental results demonstrate the method effectiveness and show its robustness when tested by different databases. Moreover, the predicted quality is more consistent with human perception.

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