H. 264 Error Resilience Coding Based on Multihypothesis Motion Compensated Prediction

[[abstract]]In this paper, we propose efficient schemes for enhancing the error robustness of multi-hypothesis motion-compensate predictive (MHMCP) coder without sacrificing the coding efficiency significantly. The proposed schemes utilize the concept of reference picture interleaving and data partitioning to make the MHMCP-coded video more resilient to channel errors, especially for burst channel error. Besides, we also propose a scheme of integrating adaptive intra-refresh into the proposed MHMCP coder to further improve the error recovery speed. Extensive simulation results show that the proposed methods can effectively and quickly mitigate the error propagation and the penalty on coding efficiency for clean channels due to the inserted error resilience features is rather minor[[fileno]]2030144030009[[department]]電機工程學

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field