Multiple description coding: compression meets the network

By Vivek K Goyal. IEEE Signal Processing Magazine, 2001

1,515 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/eefd993928c87cba381b50bd37b3b5752ee967fe

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Srinath T. V. Setty
2. Michael Walfish
3. Christos Gkantsidis
4. Pablo Rodríguez Rodríguez
5. Roman Vershynin
6. Lorenzo Alvisi
7. Yao Wang
8. Shunan Lin

In Computer Vision and Pattern Recognition

1. Roman Vershynin
2. Vivek K Goyal
3. E.J. Candes
4. Terence Tao
5. Mark Rudelson
6. Richard Baraniuk
7. Martin Reisslein
8. A. Dimakis

In Signal Processing

1. Christos Gkantsidis
2. Pablo Rodríguez Rodríguez
3. Andreas F. Molisch
4. Terence Tao
5. Touradj Ebrahimi
6. Athanassios N. Skodras
7. Charilaos A. Christopoulos
8. Yao Wang

Citations in the corpus

• Scalable and Private Media Consumption with Popcorn (2016, background): “Alternatively, Popcorn could exploit layered coding [54, 61, 83, 89] or multiple description coding (MDC) [50, 86, 100].”
• Network coding for large scale content distribution (2005, methodology): “Even when the system reaches a steady-state, network coding still provides significant benefits compared to using other techniques.”
• Femtocaching and device-to-device collaboration: A new architecture for wireless video distribution (2012, result): “While many of the results can be derived in closed form, we refer to our recent research papers [3], for details.”
• Coding Algorithms for 3DTV—A Survey (2007, background): “the overview papers [104] and [121] and the references therein).”
• Bullet: high bandwidth data dissemination using an overlay mesh (2003, methodology): “For instance, if multimedia data is being distributed to a set of heterogeneous receivers with variable bandwidth, MDC [17] allows receivers obtaining di(cid:11)erent subsets of the data to still maintain a usable multimedia stream.”
• The JPEG 2000 still image compression standard (2001, background): “Error Resilience Error resilience is one of the most desirable properties in mobile and Internet applications [25].”
• SplitStream: High-Bandwidth Content Distribution in Cooperative Environments (2003, methodology): “With appropriate data encodings such as erasure coding [3] of bulk data or multiple description coding (MDC) [13, 15 ] of streaming media, applications can thus mask or mitigate the effects of node failures even while the affected tree is being repaired.”
• Network performance evaluation using frame size and quality traces of single-layer and two-layer video: A tutorial (2004, methodology): “With Multiple Description Coding (MDC) [ 10 ] the video is encoded into several streams (descriptions).”
• Multiple Description Coding for Video Delivery (2005, methodology): “Some of these methods were also recently reviewed in [6].”
• Theoretical foundations of transform coding (2001, background): “(The last article of this issue [13] describes techniques that work when some transmitted bits are lost.”
• Distributed source coding for sensor networks (2004, methodology): “Furthermore, a recent theoretical result established multiple description coding [49] as a special case of DSC with colocated sources, with multiple descriptions easily generated by embedded coding [36] plus unequal error protection.”
• Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges (2022, neutral): “To this end, the variable-length semantic encoding, which is comparable to scalable video coding [151] and multiple description coding [152] in conventional com-”
• Error correction via linear programming (2005, background): “[3, 6, 13, 29–32, 36] and the bibliography contained therein.”