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

A convolutional neural network, image quality assessment technology, applied in image enhancement, image analysis, image data processing and other directions, can solve the problem of ignoring the importance of image spatial structure information, and achieve the effect of improving quality assessment performance

Active Publication Date: 2018-09-07
FUZHOU UNIV
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Problems solved by technology

[0003] Since the existing convolutional neural network-based image quality evaluation algorithms generally use image division to generate more data, th

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

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Embodiment Construction

[0042] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0043] The present invention provides a no-reference image quality assessment method based on convolutional neural network, such as figure 1 shown, including the following steps:

[0044] Step S1: Perform local normalization processing on the images in the training image set and the image set to be predicted. Specifically include the following steps:

[0045] Step S11: For any image, calculate the local weighted average value μ(i,j) and local weighted standard deviation σ(i,j) of the brightness value of each pixel, the calculation formula is:

[0046]

[0047]

[0048]Among them, i and j are the spatial positions of the pixels, K and L are used to define the height and width of the window during the local normalization process, and the height and width of the window are 2*K+1 and 2*L+1 respectively , k and l are the relative ...

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Abstract

The invention relates to a no-reference image quality evaluation method based on a convolutional neural network. The method comprises the following steps that: S1: carrying out local normalization processing on training images in a training image set; S2: dividing the training images into blocks of different scales in an overlapping way; S3:utilizing the convolutional neural network to learn the characteristics of blocks of different scales, and inputting a plurality of characteristics generated on different scales into three fully connected layers to learn the quality evaluation score of theblock; and S4: utilizing the trained convolutional neural network to predict the quality evaluation scores of all blocks of an image to be predicted, and calculating the average quality evaluation score of all blocks of the image to be predicted as the final quality evaluation score of the image. By use of the algorithm, the characteristics of the image on different scales are comprehensively considered, and no-reference image quality evaluation performance based on the convolutional neural network can be obviously improved.

Description

technical field [0001] The invention relates to the fields of image and video processing and computer vision, in particular to a method for evaluating image quality without reference based on a convolutional neural network. Background technique [0002] Digital images are usually affected by different degrees and types of image distortions during acquisition, compression, storage or other image processing. People hope to evaluate the distortion of the image, and then restore the image through a specific image restoration technology, thereby reducing the impact on other subsequent image processing links caused by the image distortion. At present, many unreferenced image quality assessment algorithms based on convolutional neural networks have been proposed. In 2014, Kang et al first proposed an image quality assessment algorithm based on convolutional neural network. This algorithm does not use manually extracted features like most traditional quality assessment algorithms, ...

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0002G06T2207/20081G06T2207/20084G06T2207/30168
Inventor 牛玉贞陈培坤郭文忠
Owner FUZHOU UNIV
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