Full reference image quality assessment based on convolutional neural network
US-2016358321-A1 · Dec 8, 2016 · US
US9734567B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-9734567-B2 |
| Application number | US-201514931843-A |
| Country | US |
| Kind code | B2 |
| Filing date | Nov 3, 2015 |
| Priority date | Jun 24, 2015 |
| Publication date | Aug 15, 2017 |
| Grant date | Aug 15, 2017 |
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A method for training a neural network to perform assessments of image quality is provided. The method includes: inputting into the neural network at least one set of images, each set including an image and at least one degraded version of the image; performing comparative ranking of each image in the at least one set of images; and training the neural network with the ranking information. A neural network and image signal processing tuning system are disclosed.
Opening claim text (preview).
What is claimed is: 1. A method for training a neural network to perform assessments of image quality, the method comprising: inputting into the neural network at least one set of images, each set comprising an image and at least one degraded version of the image; performing in a comparative layer a comparative ranking of each image in the at least one set of images by implementing a sigmoid function to provide pairwise ranking of the images within each set of images, the sigmoid function comprising: h ( y i , y j ) = 1 1 + ⅇ l i · j ( y i - y j ) wherein, y i and y j represent output quality scores associated with input images, x i and x j , respectively; and l i,j represents prior information for pairwise ranking of y i and y j output by the comparative layer; and training the neural network with the ranking information. 2. The method as in claim 1 , wherein learning rules for the comparative layer comprise: ∂ ∂ y i = - l i · j 1 1 + ⅇ l i · j ( y i - y j ) ( 1 - 1 1 + ⅇ l i · j ( y i - y j ) ) ; ∂ ∂ y j = l i · j 1 1 + ⅇ l i · j ( y i - y j ) ( 1 - 1 1 + ⅇ l i · j ( y i
Backpropagation, e.g. using gradient descent · CPC title
Artificial neural networks [ANN] · CPC title
Combinations of networks · CPC title
Convolutional networks [CNN, ConvNet] · CPC title
Training; Learning · CPC title
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