Robust mechanism research method of characteristic significance in image quality evaluation

Abstract

The invention discloses a robust mechanism research method of characteristic significance in image quality evaluation. The robust mechanism research method comprises the following steps: firstly, determining a target function of characteristic selection in the image quality evaluation, and initializing a model parameter; secondly, adding an optimal characteristic into a characteristic matrix, and removing a characteristic disturbance term; thirdly, calculating the significance of the characteristic selection in an image quality evaluation system; fourthly, judging whether the significance meets a system robust requirement or achieves an upper limit of a characteristic number; and finally, verifying a model classification effect. The characteristic significance is measured through an imported system characteristic signal to noise ratio, a constrained optimization problem of a smooth convex function in the image quality evaluation system is solved, interference on a classification face by non-significant characteristics is effectively lowered, the robustness of the image evaluation system is improved, and the self-adaptive optimization problem of characteristic attribute selection on the basis of an image quality evaluation network of a learning mechanism is solved.

Description

technical field [0001] The invention relates to the field of computer vision research, in particular to a robust mechanism research method of feature saliency in image quality evaluation. Background technique [0002] Image quality is an inherent attribute of an image, and is generally obtained by measuring the degree of image degradation. Image quality evaluation is a way to measure the degree of image degradation, which has wide application value in the fields of image processing, computer vision and system engineering. The application of the method has theoretical and practical significance. [0003] So far, there has not been a unified quality evaluation standard in the field of image restoration, and evaluation methods are usually divided into subjective evaluation methods and objective evaluation methods. Among them, the subjective evaluation method is easily affected by the observer's knowledge background, psychological motivation and other factors, and cannot be em...

AI Technical Summary

Problems solved by technology

The limitations of this type of method are often reflected in: in the case of fewer samples, the system cannot adaptively select reasonable feature attributes according to different evaluation objectives

That is to say, the evaluation system cannot screen the appropriate feature input according to the actual situation, and the selection of insignificant features into the system often leads to overfitting in the learning process.

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