Screen content image quality assessment using edge model
Zhangkai Ni, Lin Ma, Huanqiang Zeng, Canhui Cai, Kai‐Kuang Ma · 2016
Since the human visual system (HVS) is highly sensitive to edges, a novel image quality assessment (IQA) metric for assessing screen content images (SCIs) is proposed in this paper. The turnkey novelty lies in the use of an existing parametric edge model to extract two types of salient attributes - namely, edge contrast and edge width, for the distorted SCI under assessment and its original SCI, respectively. The extracted information is subject to conduct similarity measurements on each attribute, independently. The obtained similarity scores are then combined using our proposed edge-width pooling strategy to generate the final IQA score. Hopefully, this score is consistent with the judgment made by the HVS. Experimental results have shown that the proposed IQA metric produces higher consistency with that of the HVS on the evaluation of the image quality of the distorted SCI than that of other state-of-the-art IQA metrics.