An Image Quality Evaluation Method Based on Discrete Inseparable Shearlet Transform and Human Vision Characteristics

Abstract

The invention discloses an image quality evaluation method based on discrete inseparable shear wave transform and human visual characteristics, comprising the following steps: step 1, performing discrete inseparable shear wave transformation on the image; step 2, using discrete inseparable shear wave transform The coefficients in the shearlet transform subbands calculate the local contrast in the discrete inseparable shearlet transform domain; step three, use the contrast-sensitive function to construct the contrast detection threshold in the discrete inseparable shearlet transform domain; step four, calculate the discrete The visual concealment effect of the inseparable shearlet transform domain; step five, calculating the just-identifiable detection threshold of the discrete inseparable shearlet transform domain; step six, integrating all the differences as the objective evaluation value of the distorted image; The method uses the discrete inseparable shearlet transform to imitate the multi-channel characteristics of the human visual system, and considers the contrast-sensitive function and the visual hidden effect at the same time, forming a just identifiable detection threshold in the discrete inseparable shearlet transform domain. Difference integration is implemented using Minkowski summation and produces an objective value representing the quality of the distorted image.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to an image quality evaluation method based on discrete inseparable shear wave transform and human visual characteristics. Background technique [0002] Image quality assessment is one of the most fundamental and challenging research areas in the field of image processing. The purpose of conducting image quality evaluation research is to obtain an objective evaluation method consistent with subjective evaluation. A successful objective evaluation method can reduce workers' heavy work, such as image quality monitoring in communication, printing quality inspection, etc. In addition, in many fields, such as digital image acquisition, image compression, digital watermarking, image restoration, image color contrast enhancement, image display, etc., successful objective quality evaluation methods can improve the performance of algorithms in these fields online, while reducing computa...

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Problems solved by technology

[0019] The approximation error of the wavelet transform is smaller than the approximation error of the Fourier transform, but there is still a big gap compared with the theoretically optimal approximation error

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