Date of Award
2015
Document Type
Thesis
Degree Name
M.S. in Engineering Science
Department
Computer and Information Science
First Advisor
Byunghyun Jang
Second Advisor
Feng Wang
Third Advisor
Yixin Chen
Relational Format
dissertation/thesis
Abstract
Motion estimation is the most compute expensive part of high definition video compression. It accounts for more than 50\% of overall execution. Therefore, improving the performance of motion estimation can make significant impact on the overall performance of video compression. The performance of motion estimation can be improved in two aspects: algorithm and implementation. This thesis touches both aspects. We first propose an innovative motion estimation algorithm by replacing the traditional block matching method which comparing blocks pixel by pixel with a brand new method which based on lbp (local binary pattern) code. Our new method first encodes the original video frames into lbp code and then compares the blocks only using the lbp code. Our algorithm reduces the amount of computation significantly by avoiding many pixel by pixel comparisons present in traditional block matching approaches. Using public benchmarks our experiments show our proposed motion estimation algorithm runs 5 times faster than a traditional algorithm. Furthermore, we accelerate our proposed algorithm on gpus. Motion estimation processes of all blocks are offloaded to gpu and accelerated in parallel. Our gpu implementation runs 9 times faster than cpu implementation.
Recommended Citation
Yi, Zhaohua, "Design And Implementation Of Fast Motion Estimation In Modern Video Compression On GPU" (2015). Electronic Theses and Dissertations. 949.
https://egrove.olemiss.edu/etd/949
Concentration/Emphasis
Emphasis: Computer Science