Content-based utility function prediction for real-time MPEG-4 video transcoding
Utility function based transcoding is an efficient systematic solution for choosing optimal media transcoding operation to meet dynamic resource constraints (such as bandwidth). However, to date the real-time generation of utility function is not feasible due to computational complexity. In this paper we present a content-based utility function prediction framework for real-time MPEG-4 video transcoding. We develop a statistical approach combining real-time compressed-domain feature extraction, content-based pattern classification and regression. Our extensive experiment results demonstrate that the proposed method achieves very promising prediction accuracy - up to 89% in choosing the optimal transcoding operation with the highest quality from multiple alternatives meeting the same target bitrate.