Reference Frame Optimization for Multi-path Video Streaming using Complexity Scaling
نویسندگان
چکیده
Recent video coding standards such as H.264 offer the flexibility to select reference frames during motion estimation for predicted frames. The application of traditional Lagrangian approach to the reference frame selection problem suffers from either bounded worst-case error but high complexity, or low complexity but undetermined worst-case error. In this paper, we propose a dynamic programming based optimization algorithm that admits trade-offs between computation complexity and worst-case error using advanced rounding techniques. We also evaluate the performance of the algorithm in the context of multi-path streaming over QoS-enabled networks. Results show significant streaming quality improvement over a static reference frame selection scheme.
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