A multi-factors approach for image quality assessment based on a human visual system model

In this paper, a multi-factor full-reference image quality index is presented. The proposed visual quality metric is based on an effective Human Visual System model. Images are pre-processed in order to take into account luminance masking and contrast sensitivity effects. The proposed metric relies on the computation of three distortion factors: blockiness, edge errors and visual impairments, which take into account the typical artifacts introduced by several classes of coders. A pooling algorithm is used in order to obtain a single distortion index. Results show the effectiveness of the proposed approach and its consistency with subjective evaluations.

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