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

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 eva...

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Autores principales:	Kai Fan, Xiaoye Gu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/373fb959ef0a4eb38ff6df52a6cccc99
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spelling	oai:doaj.org-article:373fb959ef0a4eb38ff6df52a6cccc992021-11-08T02:37:18ZImage Quality Evaluation of Sanda Sports Video Based on BP Neural Network Perception1687-527310.1155/2021/5904400https://doaj.org/article/373fb959ef0a4eb38ff6df52a6cccc992021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5904400https://doaj.org/toc/1687-5273In 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.Kai FanXiaoye GuHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Kai Fan Xiaoye Gu Image Quality Evaluation of Sanda Sports Video Based on BP Neural Network Perception
description	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.
format	article
author	Kai Fan Xiaoye Gu
author_facet	Kai Fan Xiaoye Gu
author_sort	Kai Fan
title	Image Quality Evaluation of Sanda Sports Video Based on BP Neural Network Perception
title_short	Image Quality Evaluation of Sanda Sports Video Based on BP Neural Network Perception
title_full	Image Quality Evaluation of Sanda Sports Video Based on BP Neural Network Perception
title_fullStr	Image Quality Evaluation of Sanda Sports Video Based on BP Neural Network Perception
title_full_unstemmed	Image Quality Evaluation of Sanda Sports Video Based on BP Neural Network Perception
title_sort	image quality evaluation of sanda sports video based on bp neural network perception
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/373fb959ef0a4eb38ff6df52a6cccc99
work_keys_str_mv	AT kaifan imagequalityevaluationofsandasportsvideobasedonbpneuralnetworkperception AT xiaoyegu imagequalityevaluationofsandasportsvideobasedonbpneuralnetworkperception
_version_	1718442988628082688

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

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