Belief propagation based algorithms perform best in disparity estimation but suffer from high computational complexity and storage, especially in message passing. This paper proposes an efficient architecture design with three techniques to solve the problems. For the memory storage, we propose the spinning-message and the sliding-bipartite node plane that can reduce memory cost to 1.2% for image-scale algorithms and 23.4% for block-scale algorithms, when compared to the traditional approach. For the logic complexity, we propose a buffer-free processing element architecture that has 3.6 times hardware efficiency of the previous work. The three proposed techniques could be applied to various belief propagation based algorithms to save significant hardware cost as well as approach real-time speed.
|Number of pages||10|
|Journal||IEEE Transactions on Circuits and Systems for Video Technology|
|State||Published - 1 Nov 2010|
- Belief propagation
- disparity estimation