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      저자 : Danu Pranantha (검색결과 1 건)
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      • Dependent Quantization for Scalable Video Coding

        Danu Pranantha,김문철(Kim Munchurl),함상진(Hahm Sangjin),이근식(Lee Keunsik),박근수(Park Keunsoo) 한국방송·미디어공학회 2006 한국방송공학회 학술발표대회 논문집 Vol.2006 No.-

        Quantization in video coding plays an important role in controlling the bit-rate of compressed video bit-streams. It has been used as an important control means to adjust the amount of bit-streams 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-2 video coding to better maintain the quality of reconstructed frame for given constraints of target bit-rate. Since Scalable Video Coding (SVC) being currently standardized exhibits highly dependent coding nature not only between frames but also lower and higher scalability layers where the dependent quantization can be effectively applied, in this paper, we propose a dependent quantization scheme for SVC and compare its performance in visual qualities and bit-rates 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) based optimization, and construct 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, we employ pruning procedure using 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 result shows that the dependent quantization outperforms the current JSVM reference software encoder which actually takes a linear increasing QP in temporal scalability layers. The superiority of the dependent quantization is achieved up to 1.25 ㏈ increment in PSNR values and 20% bits saving for the enhancement layer of SVC.

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