Research on the Application of Multimedia Image Processing Technology in Sports Sociology Education

Abstract

In order to cope with sports events, it is difficult for cameras to accurately extract exciting moments during the competition. This article constructs a multimedia information system for sports sociology education. In terms of methodology, low-density architectures are used to measure and encode sparse signals, and the signal is reconstructed at the receiving end. By calculating the marginal probability distribution of each variable node, the reconstructed image is obtained. The experimental results show that this method performs well in detecting lens mutations and gradients, with a higher recall rate than other algorithms. The accuracy, recall rate, and F-value indicators have significantly improved, reaching 6.328%, 4.27%, and 6.012%, respectively. This method is superior to existing game shot extraction methods and lays the foundation for further detecting exciting events in sports competitions. In summary, this study has important guiding significance for the application of multimedia image processing technology in the field of sports sociology education.