No-reference image blur degree evaluation method based on multiresolution DCT edge gradient statistics

Abstract

The invention discloses a no-reference image blur degree evaluation method based on multiresolution DCT edge gradient statistics. The method includes the following steps: 1), dividing a target image into more than one 8x8 blocks and performing DCT conversion; 2), sequentially combining subblocks, identical in position, in each 8x8 DCT matrix to acquire a DCT conversion diagram containing three levels of resolution; 3), performing square summation on coefficient values of identical positions in three corresponding DCT coefficient matrixes to acquire an energy diagram identical in size; 4), performing local maximum value extraction on the energy diagram acquired on each level of resolution to acquire three edge diagrams identical in size; 5), performing gradient calculation on each edge diagram to acquire an edge gradient diagram, and performing variance statistics to acquire standard deviation S1, S2 and S3 of the edge gradient diagrams; 6), calculating to acquire a blue degree value B equal to 1 / (S1*a / (S3+e)+S2*b / (S3+e). By the method, accuracy and stability in objective evaluation of no-reference blur degree can be improved.

Description

technical field [0001] The invention relates to the field of electronic information science, in particular to a non-reference image fuzziness evaluation method based on multi-resolution DCT edge gradient statistics. Background technique [0002] Image blur is a kind of image degradation, and image blur evaluation belongs to the image quality evaluation of a specific degradation model. Accurate detection and estimation of image blur is the basis of many image / video processing methods. [0003] The types of image blur can be divided into defocus blur, motion blur, etc. according to their blur reasons. Among them, the image blur evaluation method is the subjective evaluation method of the human eye, that is, the blur degree of the image is judged and estimated subjectively by the human eye; this subjective evaluation method is relatively accurate, because the image and video are finally presented to the human eye, and the human Subjective feeling is the most important basis a...

AI Technical Summary

Problems solved by technology

Many current methods have a similar defect that accurate absolute ambiguity values ​​cannot be obtained

[0007] Of course, no evaluation method can accurately evaluate the blur of any image, which is similar to the fact that sometimes it is difficult for the human eye to distinguish the blur of two images.

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