Advanced Light Field Frame Prediction For Optimized Compression
Abstract
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.
Table of Contents
Introduction -- Background -- Related works -- Experimental and computational details -- Conclusion
Degree
M.S.