Physical Education Department, University of International Business and Economics, Beijing, China.
Comput Intell Neurosci. 2021 Oct 27;2021:5904400. doi: 10.1155/2021/5904400. eCollection 2021.
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.
在特殊的体育相机中，有子帧。镜头由多个帧组成。如果一个帧被裁剪掉，就会不清楚。视频截图的定义在于视频的质量。要获得清晰的截图，我们需要找到清晰的视频。本文的目的是分析和评估体育视频图像的质量。通过使用计算机语言对视频进行语义分析和程序设计，将视频图像与研究构建的数据模型相匹配，形成体育视频图像的实时分析，从而实现对体育技术和战术的实时分析效果。针对现有体育视频图像评价方法中存在的图像分割粗糙和空间变形率高的缺陷，提出了一种基于 BP 神经网络感知的体育视频图像评价方法。结果表明，优化算法可以克服传统算法权重收敛缓慢和变步长算法误差收敛振荡的问题。优化算法将显著降低神经网络的学习误差和网络质量分类的整体误差，从而大大提高评价的准确性。基于 BP（反向传播）神经网络感知的散手运动视频图像质量评价方法具有较高的空间精度、良好的噪声迭代性能和较低的空间变形率，因此可以准确地评价散手运动视频图像的质量。
