Toward Constructive Slepian–Wolf Coding Schemes

Guillemot, Christine; Roumy, Aline

doi:10.1016/b978-0-12-374485-2.00011-1

Cited by 5 publications

(9 citation statements)

References 44 publications

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“…Incremental coding principle: The source coding problem with one source and one side information at the decoder can be solved in practice by channel codes [26], [27], [28]. Similarly, to solve the MRA problem, we propose to construct a coding scheme based on channel codes.…”

Section: B Practical Incremental Codermentioning

confidence: 99%

Incremental Coding for Extractable Compression in the Context of Massive Random Access

Maugey

Roumy

Dupraz

et al. 2020

IEEE Trans. on Signal and Inf. Process. over Networks

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In this paper, we study the problem of source coding with Massive Random Access (MRA). A set of correlated sources is encoded once for all and stored on a server while a large number of clients access various subsets of these sources. Due to the number of concurrent requests, the server is only able to extract a bitstream from the stored data: no re-encoding can be performed before the transmission of the data requested by the clients.First, we formally define the MRA framework and propose to model the constraints on the way subsets of sources may be accessed by a navigation graph. We introduce both storage and transmission costs to characterize the performance of MRA. We then propose an Incremental coding Based Extractable Compression (IBEC) scheme. We first show that this scheme is optimal in terms of achievable storage and transmission costs. Second, we propose a practical implementation of our IBEC scheme based on rate-compatible LDPC codes. Experimental results show that our IBEC scheme can almost reach the same transmission costs as in traditional point-to-point source coding schemes, while having a reasonable overhead in terms of storage cost.

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Section: B Practical Incremental Codermentioning

confidence: 99%

Incremental Coding for Extractable Compression in the Context of Massive Random Access

Maugey

Roumy

Dupraz

et al. 2020

IEEE Trans. on Signal and Inf. Process. over Networks

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“…The encoder and the decoder are assumed to know (a good estimate of) the joint probability distribution P XY , and they are usually optimized for a particular P XY . This special form of the Slepian-Wolf coding is called asymmetric Slepian-Wolf coding [10], because the roles of X and Y are asymmetric at the decoder.…”

Section: Slepian-wolf Codingmentioning

confidence: 99%

“…3 It is also possible to regard that the concatenation of X n and MX n is the transmitted word. See [10] for more detail. information reconciliation as Slepian-Wolf encoders seems to be first considered by Muramatsu [22].…”

Section: Use Of Ldpc Codes and Open Issuesmentioning

confidence: 99%

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Problems in application of LDPC codes to information reconciliation in quantum key distribution protocols

Matsumoto

2009

Preprint

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The information reconciliation in a quantum key distribution protocol can be studied separately from other steps in the protocol. The problem of information reconciliation can be reduced to that of distributed source coding. Its solution by LDPC codes is reviewed. We list some obstacles preventing the LDPC-based distributed source coding from becoming a more favorable alternative to the Cascade protocol for information reconciliation in quantum key distribution protocols. This exposition does not require knowledge of the quantum theory.

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“…To address these problems, many methods have been proposed to joint videos collected from different cameras and use them in human detection, tracking, and recognition. For human detection, there has been considerable improvement in multi-camera methods compared to single-camera methods [7]- [10]. This improvement obviously comes from the fact that an observed human/object in a multi-camera system may appear in different views simultaneously or at different times depending on the overlaps between camera views.…”

Section: Introductionmentioning

confidence: 99%

Laplacian LRR on Product Grassmann Manifolds for Human Activity Clustering in Multicamera Video Surveillance

Wang

Gao

et al. 2017

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-In multi-camera video surveillance, it is challenging to represent videos from different cameras properly and fuse them efficiently for specific applications such as human activity recognition and clustering. In this paper, a novel representation for multicamera video data, namely the Product Grassmann Manifold (PGM), is proposed to model video sequences as points on the Grassmann manifold and integrate them as a whole in the product manifold form. Additionally, with a new geometry metric on the product manifold, the conventional Low Rank Representation (LRR) model is extended onto PGM and the new LRR model can be used for clustering non-linear data, such as multi-camera video data. To evaluate the proposed method, a number of clustering experiments are conducted on several multi-camera video datasets of human activity, including Dongzhimen Transport Hub Crowd action dataset, ACT 42 Human action dataset and SKIG action dataset. The experiment results show that the proposed method outperforms many state-of-the-art clustering methods.

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Toward Constructive Slepian–Wolf Coding Schemes

Cited by 5 publications

References 44 publications

Incremental Coding for Extractable Compression in the Context of Massive Random Access

Incremental Coding for Extractable Compression in the Context of Massive Random Access

Problems in application of LDPC codes to information reconciliation in quantum key distribution protocols

Laplacian LRR on Product Grassmann Manifolds for Human Activity Clustering in Multicamera Video Surveillance

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