We present a smoothed reference inter-layer texture prediction mode for bit depth scalability based on the
Scalable Video Coding extension of the H.264/MPEG-4 AVC standard. In our approach, the base layer encodes
an 8-bit signal that can be decoded by any existing H.264/MPEG-4 AVC decoder and the enhancement layer
encodes a higher bit depth signal (e.g. 10/12-bit) which requires a bit depth scalable decoder. The approach
presented uses base layer motion vectors to conduct motion compensation upon enhancement layer reference
frames. Then, the motion compensated block is tone mapped and summed with the co-located base layer residue
block prior to being inverse tone mapped to obtain a smoothed reference predictor. In addition to the original
inter-/intra-layer prediction modes, the smoothed reference prediction mode enables inter-layer texture prediction
for blocks with inter-coded co-located block. The proposed method is designed to improve the coding efficiency
for sequences with non-linear tone mapping, in which case we have gains up to 0.4dB over the CGS-based BDS
This paper proposes a game theoretical rate control technique for video compression. Using a cooperative gaming approach, which has been utilized in several branches of natural and social sciences because of its enormous potential for solving constrained optimization problems, we propose a dual-level scheme to optimize the perceptual quality while guaranteeing “fairness” in bit allocation among macroblocks. At the frame level, the algorithm allocates target bits to frames based on their coding complexity. At the macroblock level, the algorithm distributes bits to macroblocks by defining a bargaining game. Macroblocks play cooperatively to compete for shares of resources (bits) to optimize their quantization scales while considering the Human Visual System’s perceptual property. Since the whole frame is an entity perceived by viewers, macroblocks compete cooperatively under a global objective of achieving the best quality with the given bit constraint. The major advantage of the proposed approach is that the cooperative game leads to an optimal and fair bit allocation strategy based on the Nash Bargaining Solution. Another advantage is that it allows multi-objective optimization with multiple decision makers (macroblocks). The simulation results testify the algorithm’s ability to achieve accurate bit rate with good perceptual quality, and to maintain a stable buffer level.
For video coding in futuristic ubiquitous environments, how to efficiently manage the power consumption while preserving high video quality is crucial. To address the above challenge elegantly, we formulate a multiple objective optimization problem to model the behavior of power-distortion-optimized video coding. Though the objectives in this problem are incommensurate and in conflict with one another. By assessing the performance trade-offs as well as the collective impact of power and distortion, we propose a joint power distortion control strategy (JPDC), in which the power and distortion are jointly considered. After the analysis on the approach of solving the problem statically, we utilize a sub-optimal “greedy” approach in the JPDC scheme. Each complexity parameter is adjusted individually. The system starts coding at the highest complexity level, and will automatically migrate to lower/higher level until the performance improvement gets saturated, leading to the optimal operation point. We perform simulations to demonstrate the effectiveness of the proposed scheme. Our results show that the proposed JPDC scheme is aware of the power constraint as well as the video content, and achieves significant power savings with well-perceived video quality. Such a feature is particularly desirable for futuristic video applications.