Non-reference image quality evaluation method based on discrete cosine transform and sparse representation

Abstract

The invention discloses a non-reference image quality evaluation method based on discrete cosine transform and sparse representation to mainly solve the problem that in the prior art, non-reference image quality evaluation is not accurate. The method comprises the following steps that a gray level image is input, discrete cosine transform is carried out on the gray level image, and natural scene statistical characteristics are extracted; natural scene statistical characteristics of a series of images of different distortion types and different content are extracted, and an original characteristic dictionary is established according to the average subjective difference score; clustering is carried out on the original characteristics dictionary, and atoms are selected in a self-adaptation mode according to the tested image characteristics and the approximation degrees in the original characteristic dictionary to form a sparse representation dictionary; the tested image characteristics are solved and the sparse representation coefficients are calculated through sparse representation in characteristics space, linear weighting summation is carried out according to the subjective evaluation values in the sparse representation dictionary, and the image quality measure is obtained. The method has good consistency with the subjective evaluation result and is suitable for quality evaluation on images with various distortion types.

Description

technical field [0001] The invention belongs to the field of image processing, relates to objective evaluation of image quality, and can be used for image collection, coding compression, and network transmission. Background technique [0002] Image is an important way for human beings to obtain information. Image quality indicates the ability of image to provide information to people or equipment, and is directly related to the adequacy and accuracy of the information obtained. However, in the process of image acquisition, processing, transmission and storage, due to various factors, there will inevitably be degradation problems, which brings great difficulties to information acquisition or post-processing of images. Therefore, it is very important to establish an effective image quality evaluation mechanism. For example, it can be used for performance comparison and parameter selection of various algorithms in the process of image denoising and image fusion; in the field o...

AI Technical Summary

Problems solved by technology

Due to the limited understanding of the human visual system and the corresponding cognitive processes in the brain, the design and implementation of its algorithms are more difficult

At present, there are no reference image quality assessment methods: "Z.M.P.Sazzad, Y.Kawayoke, and Y.Horita, No-reference image quality assessment for jpeg2000based on spatial features, Signal Process" proposed by Sazzad et al. .Image Commun.,vol.23,no.4,pp.257–268,Apr.2008", but this method is only for JPEG2000 compression and not suitable for evaluating the impact of blur, noise, etc. on images

Moorthy et al proposed a learning-based approach "A.K.Moorthy and A.C.Bovik, A two-step framework for constructing blind image quality indices, IEEE Signal Process. Lett., vol.17, no.5, pp.513–516, May 2010 .”, this method directly simulates the mapping relationship between image features and quality, and its mathematical model and model parameters need to be trained or obtained manually. Since the mapping relationship cannot be accurately simulated, the final quality evaluation result is not accurate enough.

Anush Krishna Moorthy et al. proposed a method based on natural scene statistics "the Distortion Identification-based Image Verity and INtegrity Evaluation (DIIVINE) index". The evaluation results of this method are highly consistent with the subjective evaluation, but the extracted image The feature has as many as 88 dimensions, the feature extraction process takes too long, and the huge feature dimension is not convenient for the training of classification and regression models

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