Sequencing Multiple Descriptions
DCC '02 Proceedings of the Data Compression Conference
Multiple Description Coding of Predictively Encoded Sequences
DCC '02 Proceedings of the Data Compression Conference
Proceedings of the 2nd ACM international workshop on Wireless mobile applications and services on WLAN hotspots
Multiple description coding with redundant expansions and application to image communications
Journal on Image and Video Processing
Scalable multiple-description image coding based on embedded quantization
Journal on Image and Video Processing
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Robustification and optimization of a Kalman filter with measurement loss using linear precoding
ACC'09 Proceedings of the 2009 conference on American Control Conference
IEEE Transactions on Circuits and Systems for Video Technology
The effect of fading correlation on average source MMSE distortion
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
N-channel asymmetric entropy-constrained multiple-description lattice vector quantization
IEEE Transactions on Information Theory
Multiple description coding using adaptive error recovery for real-time video transmission
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part II
Multiple description coded video streaming in peer-to-peer networks
Image Communication
Multi-description multipath video streaming in wireless ad hoc networks
Image Communication
Hi-index | 754.90 |
Multiple description (MD) coding is source coding in which several descriptions of the source are produced such that various reconstruction qualities are obtained from different subsets of the descriptions. Unlike multiresolution or layered source coding, there is no hierarchy of descriptions; thus, MD coding is suitable for packet erasure channels or networks without priority provisions. Generalizing work by Orchard, Wang, Vaishampayan and Reibman (see Proc IEEE Int. Conf. Image Processing, vol.I, Santa Barbara, CA, p.608-11, 1997), a transform-based approach is developed for producing M descriptions of an N-tuple source, M⩽N. The descriptions are sets of transform coefficients, and the transform coefficients of different descriptions are correlated so that missing coefficients can be estimated. Several transform optimization results are presented for memoryless Gaussian sources, including a complete solution of the N=2, M=2 case with arbitrary weighting of the descriptions. The technique is effective only when independent components of the source have differing variances. Numerical studies show that this method performs well at low redundancies, as compared to uniform MD scalar quantization