Non-reference image quality evaluation method based on visual cortex orientation selectivity mechanism

Abstract

The invention discloses a non-reference quality evaluation method based on a visual cortex orientation selectivity mechanism, and mainly solves problems of low accuracy and poor stability of evaluation in the prior art. The method includes steps: 1, selecting experiment samples from an image database; 2, designing an image local area structural descriptor; 3, calculating an image level-one mode vector by employing the image local area structural descriptor; 4, performing dimension reduction on the level-one mode vector to obtain a level-two mode vector; 5, clustering the level-two mode vector to obtain a mode dictionary; 6, extracting training sample characteristic vectors by employing the mode dictionary; 7, establishing a prediction model by employing the training sample characteristic vectors; 8, extracting test sample characteristic vectors; 9, calculating quality values of the test samples by employing the test sample characteristic vectors and the prediction model; and 10, determining the quality of the test samples according to the quality values of the test samples. According to the method, the accuracy and the stability of quality evaluation are greatly improved, and the method can be applicable to an image processing system regarding optimization of the visual quality as the target.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a no-reference quality evaluation method, which can be used in image processing systems aimed at optimizing visual quality, such as aerial photography imaging systems, digital monitoring systems, and image compression systems. [0002] technical background [0003] With the leap-forward development of modern network communication and information technology, digital images have become the main carrier of information, and high-quality image information has brought infinite convenience to people's lives. However, due to the limited capabilities of imaging equipment, transmission channel noise, environmental noise and other factors, the original image data will be mixed with various noises in the process of multi-step processing, resulting in the attenuation of image quality, which directly affects people's perception of images. access to information. How to meas...

AI Technical Summary

Problems solved by technology

However, due to the variety of noises that destroy image quality and the complex and changeable ways of image quality attenuation, the existing no-reference quality assessment algorithms are difficult to adapt to complex scenes

The early algorithms for specific noise types are only good for testing on a single noise map, and the evaluation accuracy for multiple noise maps is low; the recent NSS algorithm and the algorithm for learning prediction models can be applied to a variety of noise maps, but The overall evaluation accuracy is not high, and the stability is poor in cross-validation and cross-library testing

These shortcomings will limit the practical application of no-reference quality assessment algorithms

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