Image definition evaluation method and image definition evaluation device based on sparse representation

Abstract

The invention relates to an image definition evaluation method and an image definition evaluation device based on sparse representation, and belongs to the image definition evaluation method and the image definition evaluation device. The method comprises the following steps that: color natural images are subjected to graying; a great number of image blocks with the same size are randomly extracted from the color natural images to be used as training signals; an overcomplete dictionary capable of expressing image internal medium-high-layer features can be obtained on the training signals by using a dictionary learning algorithm; images to be evaluated are blocked; the blocking size is identical to that of the training signals; the gradient and the variance of each image block are calculated; on the trained overcomplete dictionary, each image block gradient signal is subjected to sparse decomposition to obtain a sparse representation coefficient of the signal; the energy of each image block is pressed by L2 normal number quadratic sum of each line of elements in a sparse representation coefficient matrix; the energy of the image blocks is subjected to sequencing from great to small; the image blocks with greater energy are selected, the energy of the image blocks is subjected to normalization processing by using the corresponding variance, and the mass fraction of the images to be evaluated is obtained; and the image definition is evaluated according to the mass fraction of the images to be evaluated.

Description

technical field [0001] The present invention relates to an image definition evaluation method and device, in particular to an image definition evaluation method and device based on sparse representation. Background technique [0002] With the wide application of image information technology, image quality evaluation has become a broad and basic problem. Image is a very important way for our cognition, so the processing of image information has become an indispensable means in various fields. However, in the process of image processing and transmission, the image will inevitably be distorted and degraded, which brings great problems for people to understand the objective world and research and solve problems. Therefore, the reasonable evaluation of images is of great significance. In recent years, with the development of image processing technology, this field has attracted extensive attention of researchers. [0003] At present, image quality evaluation methods are divide...

AI Technical Summary

Problems solved by technology

The above-mentioned no-reference image sharpness evaluation method extracts image features in the spatial domain and frequency domain, and the evaluation accuracy needs to be improved

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