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.
Highlights
For network evaluation problems, there are often many qualitative factors interspersed and blended in complex evaluation problems, which require people to participate in judgment and decision-making by virtue of experience, knowledge, and wisdom [1]
BP neural network is an effective method to study nonlinear and uncertain problems. is model overcomes the shortcomings of multiple regression model and gray model and does not need to determine the mathematical expression form of mathematical model in advance, obtaining higher fitting accuracy. is paper mainly introduces the objective evaluation system of Sanda motion video image quality based on BP neural network
BP neural network trained on plane database is used as feature extractor to extract image feature parameters and combined with absolute disparity map to extract feature parameters. en, BP neural network is used for feature fusion, and a model is established to evaluate the quality of stereo images
Summary
There are often many qualitative factors interspersed and blended in complex evaluation problems, which require people to participate in judgment and decision-making by virtue of experience, knowledge, and wisdom [1]. Aiming at the existing BP neural network algorithm, this paper proposes an optimization scheme based on variable step BP neural network algorithm and applies it to the quality evaluation of Santa motion video image. Experimental results show that this method has high spatial accuracy, good noise iteration performance, and low spatial distortion rate and can accurately evaluate the quality of Santa motion video image. Literature [16] directly used the method of planar image quality evaluation in Sanda motion video image quality evaluation. Based on the improved BP neural network algorithm, this paper establishes a Sanda motion video image quality evaluation model so that the model continuously learns the inherent patterns in the training samples. After the training is successful, it can pass the input of various sports indicators and output the evaluation results
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