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.
","abstract_html":"<p>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.</p>","abstract_has_math":false,"creators":["Yi, Zhaohua"],"institution":null,"degree_name":"M.S. in Engineering Science","degree_level":"Thesis","degree_discipline":"Computer and Information Science","degree_department":null,"school":null,"contributors":["Byunghyun Jang","Feng Wang","Yixin Chen"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-01-01T08:00:00Z","date_published":"2015-01-01T08:00:00Z","updated_at":"2026-07-24T03:06:07Z","subjects":["Gpu Computing","Motion Estimation","Computer Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://egrove.olemiss.edu/etd/949","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Byunghyun Jang","Feng Wang","Yixin Chen"]},{"key":"dc:creator","label":"Author","values":["Yi, Zhaohua"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-06-20T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer and Information Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S. in Engineering Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Gpu Computing","Motion Estimation","Computer Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://egrove.olemiss.edu/etd/949"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Design And Implementation Of Fast Motion Estimation In Modern Video Compression On GPU"]}]}],"canonical_facts":{"dc:contributor":["Byunghyun Jang","Feng Wang","Yixin Chen"],"dc:creator":["Yi, Zhaohua"],"dc:date.available":["2019-06-20T07:00:00Z"],"dc:description.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.
"],"dc:identifier":["https://egrove.olemiss.edu/etd/949"],"dc:subject":["Gpu Computing","Motion Estimation","Computer Sciences"],"dc:title":["Design And Implementation Of Fast Motion Estimation In Modern Video Compression On GPU"],"thesis:degree_discipline":["Computer and Information Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S. in Engineering Science"]},"updated_at":"2026-07-24T03:06:07Z"}