Rate-distortion model for motion prediction efficiency in scalable wavelet video coding

A rate-distortion model for motion prediction efficiency in scalable wavelet video coding is proposed in this paper. The Lagrangian multiplier is widely used to solve the rate-distortion optimization problems in video coding, especially on mode decision and rate-constrained motion estimation. Different from the non-scalable video coding, the scalable wavelet video coding needs to operate under multiple bitrate conditions and it has an open-loop structure. Therefore, the conventional rate-distortion optimization technique is not suitable for the scalable wavelet case. By analyzing the rate-distortion trade-off due to different bits allocated to motion information, we propose a motion prediction gain (MPG) metric to measure motion coding efficiency. Based on the MPG metric, a new cost function for mode decision is thus proposed. Compared with the conventional Lagrangian multiplier optimization method, our experiments show that the new mode decision procedure can generally improve the PSNR performance for, particularly, the combined SNR and temporal scalability.