An image quality assessment method based on the importance of deep network features

Abstract

The invention discloses an image quality evaluation method based on the importance of deep network features. In the end-to-end neural network model training, the method adds a module for distinguishing the importance relationship between feature maps, which can predict image quality more accurately. And it shows strong generalization ability on various image quality assessment (IQA) datasets. The specific steps include: 1) preparing the image quality assessment data set used for training and testing the network model, and randomly dividing the image quality assessment data set into training set and test set according to the image content; 2) adding the SeNet module to the VGG‑16 network to build A neural network model VGG with multiple different combinations for image quality assessment * ‑SE, use the training data set to train them separately, when the trained model reaches the expected accuracy on the test data set, select this model as the final model, and save the parameters of the network model after training; 3) Use the selected final model Calculate the prediction accuracy on the test set.

Description

technical field [0001] The invention belongs to the field of computer image processing, in particular to an image quality evaluation method based on the importance among deep network features. Background technique [0002] As the basic content in the field of image processing, image quality assessment has a wide range of practicability in many fields, such as image compression, video codec, video surveillance and so on. Since it is difficult to obtain reference images of images to be evaluated in many practical applications, it is becoming increasingly important to develop effective no-reference image quality assessment (NR-IQA) methods; NR-IQA methods mainly include traditional methods and deep learning methods . Traditional methods mainly use low-level features manually extracted and related to human perception, and evaluate image quality through shallow regression methods. The main disadvantage of this method is that the manually extracted low-level features are not eno...

AI Technical Summary

Problems solved by technology

This network introduces the SeNet network used in the image classification field into the IQA problem, making it easy to distinguish the difference between the different channel features in the convolutional layer caused by sending the image into the IQA network

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