Image quality testing method based on deep convolutional neural network

A deep convolution and image quality technology, applied in image analysis, image data processing, instruments, etc., can solve problems such as difficulty in designing discrimination, and achieve the effect of reducing impact, strong generalization, and improving accuracy

Inactive Publication Date: 2014-01-29
SOUTH CHINA UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0005] The traditional method needs to extract some features of the image, and the features ultimately determine the performance of the system, and good

Method used

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  • Image quality testing method based on deep convolutional neural network
  • Image quality testing method based on deep convolutional neural network
  • Image quality testing method based on deep convolutional neural network

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Example Embodiment

[0034] Example

[0035] Such as figure 1 As shown, the image quality testing method based on the deep convolutional neural network of this embodiment includes the following steps:

[0036] (1) Establish a training sample set, and preprocess the pictures in the training sample set; the pictures in the sample set are selected from the image quality evaluation database of the Chinese University of Hong Kong (link: http: / / mmlab.ie.cuhk.edu.hk / datasets.html ), including 10,000 training pictures of good quality and poor quality. All pictures are normalized to a size of 128*128; in order to remove the three RGB (Red, Green, Blue) during the network training process For the correlation between color channels, the present invention converts RGB color space data into HSV (Hue, Saturation, Value) color space, because in the HSV color space, the correlation between each channel is small.

[0037] (2) Build a deep convolutional neural network model: such as figure 2 As shown, the deep convoluti...

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Abstract

The invention discloses an image quality testing method based on a deep convolutional neural network. The image quality testing method based on the deep convolutional neural network comprises the following steps of firstly, establishing a sample set, establishing a deep convolutional neural network model, training the deep convolutional neural network model under different initial conditions, connecting the optimal deep convolutional neural network models obtained through the multiple times of training in parallel to obtain an image quality testing system, and using the obtained image quality testing system for testing images to be tested. According to the image quality testing method based on the deep convolutional neural network, characteristic learning is carried out by simulating the learning process of human brains, the problem that characteristics are difficult to select in an existing image quality testing method is solved, the contingency of predicting results is reduced, integration is high, generalization ability is strong, and testing effects are good.

Description

technical field [0001] The invention relates to the field of image testing, in particular to an image quality testing method based on a deep convolutional neural network. Background technique [0002] With the development of electronic technology and the popularization of cameras, digital images have become a very important medium for conveying information. People have higher and higher sensory requirements for images, which put forward higher requirements for image quality, so the quality test of images is becoming more and more important. There are many standards for image quality evaluation. Generally speaking, there are mainly the following aspects: (1) Structure. Structure refers to the way all the elements that make up an image are put together. Good-quality images have relatively strong contrast, contrast between light and dark, shape contrast, and color contrast. (2) Light. The light in the background of the image directly affects the viewer's perception of the i...

Claims

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Application Information

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IPC IPC(8): G06T7/00
Inventor 郭礼华李福娣
Owner SOUTH CHINA UNIV OF TECH
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