Paper Info
Title
Distributed video coding with compressive measurements
Abstract
This paper presents a novel distributed video coding (DVC) scheme using compressive sensing (CS) that achieves low-complexity for encoding and efficient signal sensing. Most CS recovery algorithms rely only on signal sparsity. Yet, under DVC architecture, additional statistical characterization of the signal is available, which offers the potential for more precise CS recovery. First, a set of random measurements are acquired and transmitted to the decoder. The decoder then exploits the statistical characterization of the signal and generates the side information (SI). Finally, utilizing the SI, a Bayesian inference using belief propagation (BP) decoding is performed for signal recovery. The proposed CS-DVC system offers a more direct way of signal acquisition and the potential for more precise estimation of the signal from random measurements. Experimental results indicate that SI can improve the signal reconstruction quality in comparison with a CS recovery algorithm that relies only on the sparsity.
Year
DOI
Venue
2011
10.1145/2072298.2071992
ACM Multimedia 2001
Keywords
Field
DocType
cs recovery algorithm,signal reconstruction quality,video coding,compressive measurement,efficient signal,additional statistical characterization,dvc architecture,signal recovery,signal acquisition,signal sparsity,precise cs recovery,random measurement,compressed sensing,signal reconstruction,belief propagation,random measure,compressive sampling,sparse representation,bayesian inference
Computer vision,Bayesian inference,Computer science,Side information,Coding (social sciences),Artificial intelligence,Decoding methods,Signal reconstruction,Compressed sensing,Belief propagation,Encoding (memory)
Conference
Citations 
PageRank 
References 
2
0.37
6
Authors
3
Name
Order
Citations
PageRank
Hsiao-yun Tseng1101.35
Yun-Chung Shen2346.30
Ja-ling Wu31569168.11