Adaptive Lagrange multiplier selection model in rate distortion optimization for 3D wavelet-based scalable video coding

In this paper, a novel adaptive Lagrange multiplier selection model in rate-distortion optimization (RDO) is proposed to determine the motion estimation mode during motion compensated temporal filter (MCTF) decomposition in the context of 3D wavelet-based scalable video codec (SVC). First, the motion activity of temporal subbands is investigated. Then, the model parameters for different MCTF levels are estimated. Finally, an optimization model for each temporal subband is obtained from the adaptive Lagrange multiplier selection. We demonstrate the accuracy and performance of our proposed model through extensive numerical simulations. Experimental results illustrate that the proposed model is adaptive with the characteristics of the temporal subbands, suggesting that our model can effectively improve the video quality in terms of both the PSNR and the mean structural similarity index (mean SSIM).