Three-dimensional image quality evaluation method based on sparse binocular fusion convolutional neural network

Abstract

The invention discloses a three-dimensional image quality evaluation method based on a sparse binocular fusion convolutional neural network, and the method comprises the following steps: S1, constructing a three-dimensional image quality evaluation network based on the binocular fusion convolutional neural network, and the network comprises left and right branches and a fusion branch; and S2, applying a structured sparse constraint to each layer of the binocular fusion convolutional neural network, wherein an objective function of network optimization is shown as a formula (1). The three-dimensional image quality evaluation method of the patent is more accurate and efficient, better fits the human eye perception quality, is higher in operation speed, and promotes the development of a three-dimensional imaging technology to a certain extent.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to the improvement and optimization of a stereoscopic image quality evaluation method, and the optimization of the calculation speed of a convolutional neural network for stereoscopic image quality evaluation, in particular to a stereoscopic image quality based on a sparse binocular fusion convolutional neural network evaluation method. Background technique [0002] Since viewing degraded stereoscopic images will cause visual fatigue and dizziness, stereoscopic image quality evaluation has become an urgent matter to be solved [1]. Stereoscopic image quality evaluation needs to consider factors such as depth information, disparity information, and binocular competition. Compared with planar image quality evaluation, stereoscopic image quality evaluation is more challenging. Generally, stereoscopic image quality evaluation can be divided into two methods: subjective evaluation and objec...

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But almost no one has applied SSL to the deep learning network for stereo image quality evaluation

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