A novel SVD-based image quality assessment metric

Image distortion can be categorized into two aspects: content-dependent degradation and content-independent one. An existing full-reference image quality assessment (IQA) metric cannot deal with these two different impacts well. Singular value decomposition (SVD) as a useful mathematical tool has been used in various image processing applications. In this paper, SVD is employed to separate the structural (content-dependent) and the content-independent components. For each portion, we design a specific assessment model to tailor for its corresponding distortion properties. The proposed models are then fused to obtain the final quality score. Experimental results with the TID database demonstrate that the proposed metric achieves better performance in comparison with the relevant state-of-the-art quality metrics.