Advanced Light Field Frame Prediction For Optimized Compression

Current light field compression techniques lack robustness to handle both rate distortion optimized motion compensation as well as latency during the encoding and decoding process. This paper focuses on a contribution approach that uses advanced prediction with affine and translational motion models and optimized view prediction structures. This method allows a significant compression performance gain over the current state of art of hierarchical temporal coding by 13.9%. The proposed method introduces an optimized encoding order that takes advantage of each group of pictures structure in order to leverage the dense perspective model of light field imagery. Both a global perspective model and a local affine model can be combined to show substantial distortion reduction at low processor costs. This contribution approach leads to an efficient and robust compression scheme for light field datasets.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Electrical Engineering (UMKC)

Grantor dc:publisher

University of Missouri--Kansas City

Year dc:date.issued

2017

Author dc:creator

• Cornwell, Eric

Advisor dc:contributor.advisor

• Li, Zhu

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/10355/60658

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/60658