Modeling of rate and perceptual quality of video and its application to frame rate adaptive rate control
In a prior work, we have developed both rate and perceptual quality models for temporal and amplitude (i.e., SNR) scalable video produced by the H.264/SVC encoder. In this paper, we validate from experimental data that the functional form of the rate model is applicable to H.264/AVC encoded video, which has the same temporal scalability but no SNR scalability, but the model parameter values differ. We further investigate how to predict both rate and quality model parameters using content features computed from the original video. Experimental data show that with proper feature combination, we can estimate the model parameters very accurately, and the estimated bit rate and quality using the predicted model parameters match with the measured bit rate and quality with high Pearson correlation (PC) and small root mean square error (RMSE). We have implemented a simple pre-processor in the H.264/AVC encoder to guide the frame rate adaptive rate control. Results show that our model-based frame rate adaptive rate control outperforms the default rate control algorithm with better quality.