A Content-Adaptive Distortion-Quantization Model for H.264/AVC and its Applications

Accurately estimating the resultant quality or distortion associated with quantization parameter (QP) is very helpful to video encoding. In this research, a content-adaptive distortion-quantization model for H.264/AVC is proposed to predict the distortion level, which is defined as the difference between the original video frame and the decoded one in the sum of squared errors. The proposed model has only one adjustable parameter related to the macroblock content and provides a mapping between QP and the corresponding distortion before the exact encoding process. Given a targeted frame quality measured in peak signal to noise ratio (PSNR), this model can help to assign a suitable QP value to each frame. Two applications are then presented, i.e., the single-pass constant frame PSNR coding and the two-pass coding with the additional bitrate or storage constraint, both of which may facilitate such applications of video archiving and editing. The experimental results show that the targeted PSNR of each decoded frame can be achieved effectively by the proposed mechanism.