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. 2021 Oct 27:2021:5904400.
doi: 10.1155/2021/5904400. eCollection 2021.

Image Quality Evaluation of Sanda Sports Video Based on BP Neural Network Perception

Kai Fan  1 , Xiaoye Gu  1
Affiliations

Image Quality Evaluation of Sanda Sports Video Based on BP Neural Network Perception

Kai Fan et al. Comput Intell Neurosci. .

Abstract

In the special sports camera, there are subframes. A lens is composed of multiple frames. It will be unclear if a frame is cut out. The definition of video screenshots lies in the quality of video. To get clear screenshots, we need to find clear video. The purpose of this paper is to analyze and evaluate the quality of sports video images. Through the semantic analysis and program design of video using computer language, the video images are matched with the data model constructed by research, and the real-time analysis of sports video images is formed, so as to achieve the real-time analysis effect of sports techniques and tactics. In view of the defects of rough image segmentation and high spatial distortion rate in current sports video image evaluation methods, this paper proposes a sports video image evaluation method based on BP neural network perception. The results show that the optimized algorithm can overcome the slow convergence of weights of traditional algorithm and the oscillation in error convergence of variable step size algorithm. The optimized algorithm will significantly reduce the learning error of neural network and the overall error of network quality classification and greatly improve the accuracy of evaluation. Sanda motion video image quality evaluation method based on BP (back propagation) neural network perception has high spatial accuracy, good noise iteration performance, and low spatial distortion rate, so it can accurately evaluate Sanda motion video image quality.

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Conflict of interest statement

The authors declare that they have no conflicts of interest regarding this work.

Figures

Figure 1
Figure 1
Logical structure of BP neural network.
Figure 2
Figure 2
The overall framework of the evaluation system.
Figure 3
Figure 3
Scatter diagram of image quality evaluation model based on BP neural network.
Figure 4
Figure 4
The degree of fit between the subjective evaluation results of the training samples and the objective evaluation results.
Figure 5
Figure 5
The degree of fit between the subjective evaluation results of the test sample and the objective evaluation results.
Figure 6
Figure 6
Network training/testing SSE curve.
Figure 7
Figure 7
Simulation comparison of image segmentation filtering optimization.
Figure 8
Figure 8
Subjective and objective evaluation values of the experimental training set.
Figure 9
Figure 9
Subjective and objective evaluation values of the experimental test set.
See this image and copyright information in PMC

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