Label-free non-reference image quality assessment via deep neural network

US9734567B2 · US · B2

Patent metadata
FieldValue
Publication numberUS-9734567-B2
Application numberUS-201514931843-A
CountryUS
Kind codeB2
Filing dateNov 3, 2015
Priority dateJun 24, 2015
Publication dateAug 15, 2017
Grant dateAug 15, 2017

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  1. Title

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Abstract

Official abstract text for this publication.

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.

First claim

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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

Assignees

Inventors

Classifications

  • G06N3/084Primary

    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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What does patent US9734567B2 cover?
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 ne…
Who is the assignee on this patent?
Samsung Electronics Co Ltd
What technology area does this patent fall under?
Primary CPC classification G06N3/084. Mapped technology areas include Physics.
When was this patent published?
Publication date Tue Aug 15 2017 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 2 related publications on this page (citations in our corpus or others sharing the same primary CPC).