Adaptive Lagrangian Multiplier for Quantization Parameter Cascading in HEVC Hierarchical Coding

Hierarchical prediction structures applied in High Efficiency Video Coding (H.265/HEVC) leads to significant coding efficiency improvement. Quantization parameter cascading (QPC) techniques designed for specific prediction structure employs different quantization intervals for different temporal layers that further improve rate-distortion (R-D) performance of hierarchical coding. The existing QPC schemes focus on quantization parameter allocation, however, ignore the adaptation of Lagrangian multiplier to prediction structure, inter-layer residual dependency and layer-different quantization parameters (QPs). The misalignment of employed Lagrangian multiplier to the optimal one restricts the performance of QPC methods. To promote QPC schemes to their best performance, we propose a Lagrangian multiplier adaptation method based on accurate hyperbolic R-D model, distortion-QP model and rate-QP model. The derived Lagrangian multiplier is adaptive to residual characteristics under different prediction structures and QPC methods. Experimental results show that our proposed Lagrangian multipliers applied to existing QPC methods achieves Bjontegaard delta bit rate (BDBR) gain of 3.59% to 4.65% with small computational complexity increase.