Joint learning for side information and correlation model based on linear regression model in distributed video coding

Authors:
Xianming Liu;Debin Zhao;Yongbing Zhang;Siwei Ma;Qingming Huang;Wen Gao
Affiliations:
Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, P.R. China;Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, P.R. China;Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, P.R. China;Institute of Digital Media, Peking University, Beijing, P.R. China;Graduate University, Chinese Academy of Science, Beijing, P.R. China;Institute of Digital Media, Peking University, Beijing, P.R. China
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 3
Cited 0

Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Least-square prediction for backward adaptive video coding

EURASIP Journal on Applied Signal Processing
Correlation Noise Modeling for Efficient Pixel and Transform Domain Wyner–Ziv Video Coding

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

The coding efficiency of distributed video coding system is significantly determined by the side information quality and correlation model. Motivated by theoretical analysis of the maximum likelihood treatment for linear regression model, we propose a novel joint online learning model for side information generation and correlation model estimation in this paper. In our proposed scheme, each pixel in the side information is approximated as the linear weighted combination of samples within a local spatio-temporal neighboring space. Weights are trained in a self-feedback fashion, during which the correlation model parameters can also be achieved. The efficiency of the proposed joint learning model is confirmed experimentally.