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A new objective, full-reference metrics of image quality is proposed in this  paper. It should match perceptual (subjective) image quality assessment in a  better way. The proposed method consists of two quality measures which  separately indicate image quality on edges and in texture areas which are  calculated in a three-step algorithm. The “soft mask” is initially found for  separation in edge and texture areas. Then, two MSEs (mean square error) with  corresponding two PSNRs (peak signal-to-noise ratio) for edge and texture are  calculated using soft mask as the weighting factor. Finally, the obtained two  PSNRs are re-calculated into the two quality indices for edges and texture.  Additionally, the separation factor, defined as percentage of edge areas in  image, is considered, describing the influence of the image content on  perceptual assessment. The proposed 2D metrics is especially suited for  evaluations of different interpolation and compression algorithms.

Edge-texture 2D image quality metrics suitable for evaluation of image interpolation algorithms