ICA sparse representation and SOM-based distorted image quality evaluation method

Abstract

The invention discloses an ICA sparse representation and SOM-based distorted image quality evaluation method. The method comprises the steps of 1) performing ICA sparse representation on a reference image and a to-be-tested image to obtain sparse representation information of the images; 2) calculating an SSIM value between the reference image and the to-be-tested image after the sparse representation; 3) drawing a scatter diagram of the SSIM value and a DMOS value; 4) clustering scatter diagram data by using an SOM algorithm: classifying the data distributed more intensively into one category, and classifying other data into the other category; 5) using a cross validation regression algorithm for each category of the data, and performing regression mapping on the SSIM value in the step 4)to obtain the DMOS value; 6) calculating an error value between the DMOS value of the two categories of the data and a DMOS value in an actual database; and 7) performing weighted average on the obtained error value to serve as an index value of final image quality evaluation.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a distorted image quality evaluation method based on ICA sparse representation and SOM. Background technique [0002] Image quality evaluation methods are divided into subjective quality evaluation methods and objective quality evaluation methods. The subjective quality evaluation method is that the observers rate the visual quality of the image after observing the image. Subjective quality evaluation is most consistent with the visual system of the human eye, but is not suitable for practical systems. Objective quality evaluation is a visual quality score made by using a mathematical model to calculate the input image. The consistency between objective quality evaluation and subjective quality evaluation is the only criterion to measure the pros and cons of objective quality evaluation methods. According to the standard of whether reference image information is needed, objecti...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to overcome the problem that most of the existing image quality evaluation methods are not suitable for multi-distorted images, and propose a multi-distortion method based on ICA (Independent Component Analysis, ICA) sparse representation and self-organizing mapping SOM (Self Organizing mapping, SOM) Evaluation Method of Distorted Image Quality

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