Dependent quantization for scalable video coding

Abstract

Quantization in video coding plays an important role in controlling the bitrates of compressed video bitstreams. It has been used as an important control means to adjust the amount of bitstreams to allowed bandwidth of delivery networks and storage. Due to the dependent nature of video coding, dependent quantization has been proposed and applied for MPEG video coding to better maintain the quality of reconstructed frames for given constraints of target bitratess. Since Scalable Video Coding (SVC) being currently standardized exhibits highly dependent coding nature not only between frames but also between lower and higher scalability layers where the dependent quantization can be effectively applied. In this thesis, we propose a dependent quantization scheme for SVC and compare its performance in visual qualities and bitratess with the current JSVM reference software for SVC. The proposed technique exploits the frame dependences within each GOP of SVC scalability layers to formulate dependent quantization. We utilize Lagrange optimization which is widely accepted in R-D (rate-distortion) optimization, and construct a trellis graph to find the optimal cost path in the trellis by minimizing the R-D cost. The optimal cost path in the trellis graph is the optimal set of quantization parameters (QP) for frames within a GOP. In order to reduce the complexity in searching such optimal paths in the trellis graph, we employ a pruning procedure using the monotonicity property in the trellis optimization and cut the frame dependency into one GOP to decrease dependency depth. The optimal Lagrange multiplier that is used for SVC is equal to H.264/AVC which is also used in the mode prediction of the JSVM reference software. The experimental results show that the dependent quantization outperforms in terms of PSNR values and bitamounts the current JSVM reference software encoder which actually takes a linear increasing QP in temporal scalability layers. The superiority of the ...