Blind image quality assessment using a reciprocal singular value curve

• We found out relationship between image distortion and reciprocal singular value curve.
• We constructed two new general blind image quality assessment (IQA) indices that respectively use the area and curvature of image reciprocal singular value curves.
• The proposed indices have the following advantages. (1) Simple mathematical expression leads to low computational complexity; (2) they can be applied to more distorted categories, such as “High frequency noise,” and “WN-color.”

The reciprocal singular value curves of natural images resemble inverse power functions. The bending degree of the reciprocal singular value curve varies with distortion type and severity. We describe two new general blind image quality assessment (IQA) indices that respectively use the area and curvature of image reciprocal singular value curves. These two methods almost require very little prior knowledge of any image or distortion nor any process of training, and they can handle multiple unknown distortions, hence they are no-training methods. Experimental results on five simulated databases show that the proposed algorithms deliver quality predictions that have high correlation with human subjective judgments, and that are competitive with other blind IQA models.