Bit rate selection technology of image processing based on artificial intelligence in MPEG-DASH adaptive streaming media

Aiming at the bit rate selection problem of MPEG-DASH adaptive streaming media in image processing, a hybrid method combining multiple artificial intelligence algorithms is proposed. Firstly, kernel principal component analysis, Grey Wolf optimization algorithm and least squares support vector machine are integrated to construct an efficient hybrid algorithm model. This model aims to optimize the image processing effect in streaming media transmission, especially in the dynamic network environment. The experimental results show that the accuracy of the hybrid algorithm reaches 0.945 in the training process, and the absolute error is only 0.0005, which is significantly better than other comparison algorithms. Further empirical analysis shows that the accuracy of the proposed rate selection technique in image processing is as high as 92.3%, which is far higher than the existing technique. This research not only improves the image quality of streaming media transmission, but also greatly improves the user experience. The research provides a new perspective for image processing technology in the field of digital media, and is of great significance for promoting the innovation and development of streaming media technology.