No-reference image quality evaluation method based on convolutional neural network

Abstract

The invention discloses a no-reference image quality evaluation method based on a convolutional neural network. The method comprises the steps: preprocessing a distortion graph and a natural graph to obtain a similar graph, and then constructing a neural network according to the distortion graph and the similar graph; on the basis of the confrontation generation concept of a GAN framework, in a generation network part, integrating the jump connection characteristic of a U-net framework and the denseblock structure characteristic of a densenet framework; in the network discrimination part, adopting a simple classification network; and finally training the constructed neural network. According to the method, the characteristics of the GAN network, the U-net network and the densenet network are respectively absorbed and combined, a more effective neural network is constructed, conversion and migration from graphs to graphs are more effectively realized, a better result is obtained in graph-to-graph realization, and the simulated quality score and the real quality score have strong correlation and smaller error.

Description

technical field [0001] The invention belongs to the field of image processing, designs an image quality evaluation method, and relates to the application of a generation confrontation network in deep learning in image quality evaluation. Background technique [0002] Nowadays, with the rapid development of Internet technology and communication technology, digital images have become an important way of information transmission in people's daily life. According to statistics, since 2011, the total number of digital photos produced in the world has reached tens of billions, and this number is still increasing year by year. However, images are susceptible to different kinds of distortions during the process of acquisition, storage, compression, and transmission, resulting in reduced image quality. Therefore, how to evaluate image quality accurately and reliably has become an important research hotspot in current and future research. Usually, most images are viewed by people, s...

AI Technical Summary

Problems solved by technology

[0005] Most of the existing no-reference quality evaluation methods are evaluation methods with known subjective quality scores. Such methods usually require a large number of training sample images and corresponding subjective scores to train the quality prediction model. In contrast, subjective quality scores are unknown. There are still few reference-free evaluation methods and the performance of existing methods is still not comparable to methods with known subjective quality scores

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