Paper Info
Title
Lie Group Transformation Models for Predictive Video Coding
Abstract
We propose a new method for modeling the temporal correlation in videos, based on local transforms realized by Lie group operators. A large class of transforms can be theoretically described by these operators, however, we propose to learn from natural movies a subset of transforms that are statistically relevant for video representation. The proposed transformation modeling is further exploited to remove inter-view redundancy, i.e., as the prediction step of video encoding. Since the Lie group transformation coefficients are continuous, a quantization step is necessary for each transform. Therefore, we derive theoretical bounds on the distortion due to coefficient quantization. The experimental results demonstrate that the new prediction method with learned transforms leads to better rate-distortion performance at higher bit-rates, and competitive performance at lower bit-rates, compared to the standard prediction based on block-based motion estimation.
Year
DOI
Venue
2011
10.1109/DCC.2011.93
DCC
Keywords
Field
DocType
standard prediction,predictive video coding,lie group transformation models,higher bit-rates,lie group operator,competitive performance,lie group transformation coefficient,proposed transformation modeling,new method,new prediction method,lower bit-rates,prediction step,motion estimation,quantization,lie group,correlation,rate distortion theory,encoding,entropy,transformative learning,lie groups,predictive models
Lie group,Computer vision,Computer science,Theoretical computer science,Redundancy (engineering),Operator (computer programming),Artificial intelligence,Motion estimation,Quantization (signal processing),Distortion,Rate–distortion theory,Encoding (memory)
Conference
ISSN
Citations 
PageRank 
1068-0314
1
0.45
References 
Authors
10
4
Name
Order
Citations
PageRank
Ching Ming Wang110.45
Jascha Shol-Dickstein210.45
Ivana Tosic38011.83
Bruno A. Olshausen449366.79