Distributed video coding using compressive sampling
PCS'09 Proceedings of the 27th conference on Picture Coding Symposium
Distributed compressed video sensing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Bayesian compressive sensing via belief propagation
IEEE Transactions on Signal Processing
Correlation Noise Modeling for Efficient Pixel and Transform Domain Wyner–Ziv Video Coding
IEEE Transactions on Circuits and Systems for Video Technology
A hierarchical control scheme for energy quota distribution in hybrid distributed video coding
Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
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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.