Visual Quality and File Size Prediction of H.264 Videos and Its Application to Video Transcoding for the Multimedia Messaging Service and Video on Demand

In this paper, we address the problem of adapting video files to meet terminal file size and resolution constraints while maximizing visual quality. First, two new quality estimation models are proposed, which predict quality as function of resolution, quantization step size, and frame rate parameters. The first model is generic and the second takes video motion into account. Then, we propose a video file size estimation model. Simulation results show a Pearson correlation coefficient (PCC) of 0.956 between the mean opinion score and our generic quality model (0.959 for the motion-conscious model). We obtain a PCC of 0.98 between actual and estimated file sizes. Using these models, we estimate the combination of parameters that yields the best video quality while meeting the target terminal's constraints. We obtain an average quality difference of 4.39% (generic model) and of 3.22% (motion-conscious model) when compared with the best theoretical transcoding possible. The proposed models can be applied to video transcoding for the Multimedia Messaging Service and for video on demand services such as YouTube and Netflix.