Paper Info
Title
Spatial non-stationary correlation noise modeling for Wyner-Ziv error resilience video coding
Abstract
Most of the Wyner-Ziv (WZ) video coding schemes in literature model the correlation noise (CN) between original frame and side information (SI) by a given distribution whose parameters are estimated in an offline process. In this paper, an online CN modeling algorithm is proposed towards a more practical WZ-based error resilient video coding (WZ-ERVC). In ERVC scenario, the side-information is typically generated from the error concealed picture instead of bi-directional motion prediction. The proposed online CN modeling algorithm achieves the so-called classification gain by exploiting the spatially non-stationary characteristics of the motion field and texture. The CN between the source and error concealed SI is modeled by a Laplacian mixture model, where each mixture component represents the statistical distribution of prediction residuals and the mixing coefficients portray the motion vectors estimation error. Experimental results demonstrate significant performance gains both in rate and distortion versus the conventional Laplacian model.
Year
DOI
Venue
2009
10.1109/ICIP.2009.5413508
ICIP
Keywords
Field
DocType
correlation noise modeling,statistical distributions,spatial nonstationary correlation noise modeling,motion vectors estimation error,spatial non-stationary correlation noise,parameter estimation,statistical distribution,laplacian mixture model,wyner-ziv coding,online cn modeling algorithm,motion field,motion estimation,image classification,wyner-ziv error resilience video,video coding,conventional laplacian model,bi-directional motion prediction,literature model,wyner-ziv error resilience video coding,online correlation noise modeling algorithm,mixture component,image texture,bidirectional motion prediction,practical wz-based error,spatial nonstationary,proposed online cn modeling,decoding,mixture model,silicon,pixel,correlation
Computer vision,Motion field,Pattern recognition,Image texture,Computer science,Probability distribution,Artificial intelligence,Motion estimation,Estimation theory,Decoding methods,Distortion,Mixture model
Conference
ISSN
ISBN
Citations 
1522-4880 E-ISBN : 978-1-4244-5655-0
978-1-4244-5655-0
2
PageRank 
References 
Authors
0.37
6
4
Name
Order
Citations
PageRank
Yongsheng Zhang120443.58
Hongkai Xiong251282.84
Li Song332365.87
Yu Song435652.74