Convolutional-neural-network-based reference-free three-dimensional image quality evaluation method

A convolutional neural network, stereo image technology, applied in the field of reference-free stereo image quality assessment, can solve problems such as difficulty in implementation and inability to meet

Inactive Publication Date: 2015-12-16
SHANDONG UNIV
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Problems solved by technology

[0007] (3) No reference model: The no-reference algorithm that does not depend on the original reference image is more in line with the actual application scenario, but its implementation is also the most difficult

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Embodiment Construction

[0036] The present invention is described in detail below in conjunction with accompanying drawing:

[0037] Convolutional Neural Network Design:

[0038] The task of this paper is to find the internal relationship between image quality and image features through 2D images, and apply it to stereo image pairs, and use the regression algorithm to obtain the evaluation of stereo image quality through the high-level features of the left and right images obtained respectively.

[0039] (1) Preprocessing of 2D images

[0040] In the process of training the network and testing, most of the pictures in the selected LIVE2D database are 768×512 in size, and the overall picture entering the network will greatly affect the network speed and accuracy. Considering that the degree of distortion is the same everywhere in the picture, the original picture is divided into 32×32 picture blocks of the same size, and processed by local normalization method, which will weaken the influence of pict...

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Abstract

The invention discloses a convolutional-neural-network-based reference-free three-dimensional image quality evaluation method. The method comprises the following steps: carrying out pretreatment on a 2D image; to be specific, inputting an image block obtained by pretreatment into a deep convolutional neural network, carrying out convolutional pooling processing to obtain an advanced feature of the image block, and then carrying out BP training by using the neural network based on a mass fraction of an original image so as to obtain a parameter of a base model; obtaining a corresponding basic model according to the parameter of the base model, carrying out centering on a three-dimensional image and carrying out pretreatment identical with that of the 2D image on the left image and the right image, and inputting the image blocks at the same position into the basic module simultaneously to obtain a corresponding advanced feature vector; and carrying out testing under a LIVE 3D database. On the basis of the reference-free evaluation algorithm, a good result better that the existing quality evaluation result can be obtained.

Description

technical field [0001] The invention relates to a no-reference stereoscopic image quality assessment method based on a convolutional neural network. Background technique [0002] In recent years, with the rapid development of multimedia technology, 3D stereoscopic images have entered people's lives in various forms. The realistic on-the-spot experience and shocking visual effects brought by 3D stereoscopic images are incomparable with 2D images. It not only brings people immersive visual enjoyment, but also arouses people's interest and awareness of external things. At the same time, the widespread use of stereoscopic images also puts forward higher requirements for algorithms for evaluating the quality of stereoscopic images. [0003] Stereoscopic image quality evaluation occupies a very important position in stereoscopic images. It can not only judge the pros and cons of processing algorithms in stereoscopic images, but also optimize and design algorithms to improve the e...

Claims

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T2207/20081
Inventor 张伟瞿晨非马林张海峰张伟东
Owner SHANDONG UNIV
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