Three-dimensional image quality evaluation method based on sparse binocular fusion convolutional neural network
A convolutional neural network and stereoscopic image technology, applied in the field of image processing, to achieve the effect of speeding up computing, reducing computing complexity, and improving evaluation performance
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[0029] The present invention uses the public stereoscopic image library LIVE 3D Phase I and LIVE 3D Phase II to conduct experiments. The LIVE 3D Phase I image library contains 20 original stereoscopic image pairs and 365 symmetrically distorted stereoscopic image pairs. The distortion types include JPEG compression, JPEG 2000 compression, Gaussian blur Gblur, Gaussian white noise WN and fast decay FF. The DMOS values are distributed in - 10 to 60. The LIVE 3D Phase II image library contains 8 original stereoscopic image pairs and 360 symmetrically distorted and asymmetrically distorted stereoscopic image pairs, of which 120 pairs are symmetrically distorted stereoscopic images, and 240 pairs are asymmetrically distorted stereoscopic images, and the distortion types include JPEG compression , JPEG 2000 compression, Gaussian blur Gblur, Gaussian white noise WN and fast decay FF, the DMOS value is distributed from 0 to 100.
[0030] The method is described in detail below in...
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