Research on anti-conflict extraction method of multimedia video information based on machine learning

In order to solve the problems of long time, poor denoising performance, low quality of multimedia video information and low efficiency of information extraction in current methods of extracting multimedia video information. An anti-collision extraction method of multimedia video information based on machine learning is proposed, which combines K-SVD algorithm and Batch-OMP algorithm to remove noise in multimedia video information. Considering the error concealment method used by decoder, channel condition, importance and lifetime of multimedia video information package, C uses improved proportional fair scheduling algorithm to complete the scheduling of multimedia video information. It calculates the time curve of multimedia video information, gets the local extremum points in the time curve, determines the length and location of the extracted video clips according to the local extremum points, calculates the importance of the video clips, and completes the extraction of multimedia video information according to the importance. In order to verify the effectiveness of the proposed method, a simulation experiment is carried out. The experimental results show that compared with the traditional methods, the proposed method has better denoising performance and higher information extraction efficiency. The above results show that the proposed method has good application prospects.