Fast Inter-Mode Decision Based on Rate-Distortion Cost Characteristics.
ABSTRACT In this paper, a new fast mode decision (FMD) algorithm is proposed for the state-of-the-art video coding standard H.264/AVC.
Firstly, based on Rate-Distortion (RD) cost characteristics, all inter modes are classified into two groups, one is Skip mode
(including both Skip and Direct modes) and all the other inter modes are called non-Skip modes. In order to select the best
mode for coding a Macroblock (MB), minimum RD costs of these two mode groups are predicted respectively. Then for Skip mode,
an early Skip mode detection scheme is proposed; for non-Skip modes, a three-stage scheme is developed to speed up the mode
decision process. Experimental results demonstrate that the proposed algorithm has good robustness in coding efficiency with
different Quantization parameters (Qps) and various video sequences and is able to achieve about 54% time saving on average
while with negligible degradation in Peak-Signal-to-Noise-Ratio (PSNR) and acceptable increase in bit rate.
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ABSTRACT: The motion estimation and disparity estimation are used to remove the temporal and inter-view redundancies in multiview plus depth video coding, however, the variable block-size ME and DE make the computational complexity increase dramatically. This drawback limits it to be applied in real-time applications. In this paper, based on the mode correlations between depth video and its corresponding texture video, motion prediction and coded block pattern, we propose a fast mode decision algorithm to reduce the computational complexity of multiview depth video coding. Experimental results show that the proposed algorithm can achieve 67.18 and 69.90 % encoding time saving for even and odd views, respectively, while maintaining a comparable rate-distortion performance. In addition, with the dramatic encoding time reduction, the proposed algorithm becomes more suitable for real-time applications.Journal of Real-Time Image Processing 01/2013; DOI:10.1007/s11554-013-0328-3 · 1.11 Impact Factor