Architecture Design of Belief Propagation for Disparity Estimation

Technology changes life, and this is a sentence form advertisement, it reflects the real lief of ours. From Apple to create the personal computer era, and then to the Cisco network equipment company, represented by opening the door of the Internet, then entered into with Qualcomm as the representative of the era of mobile communications. Last five years, smartphone blowout type growth in the context of mobile Internet, smart phones as the representative of the personal portable computing devices tend to grow gradually end. And in VR or AR for the technical background of the computing experience platform seems to notice the flash point of the next computing terminal, 3D effecte requirements in these applications are very intuitive and urgent. The disparity estimation is a part of the problem. For abovementioned applications, disparity estimation is the key technology to obtain 3D information. Stereo vision approach is more power efficient which is more suitable for mobile devices than structure lighted and Time-of-Flight (ToF). Among existing global optimization algorithms, Field is widely discussed because of its promising performance. Disparity estimation can be transformed into a prediction based on 2D field with energy minimization problem in mathematics. There are many algorithms which can solve the disparity estimation problems, such as: dynamic programming algorithm, graph cut algorithm, belief propagation algorithm, and so on. Belief propagation algorithm provides a good quality depth map of the image, the algorithm its computation is regular, so that the belief propagation algorithms different to other algorithms, making it very suitable for implementation as ASIC. The belief propagation algorithm, it assumed that the depth value for each pixel related to itself and all of the values of surrounding pixel, passed through the belief value and iteration between pixels to determine the present depth values of the pixels. In this thesis, the character of Belief Propagation is reanalyzed. A disparity estimation engine supports full-HD 60 fps is proposed. According to the software experiment, the proposed disparity range is set to 64, and so is other parameter setting. In order to support the full-HD 60 fps specification, in some parts of architecture design insert 2-Pipeline. As the result, the proposed architecture improves operation frequency in almost 300%.