Graph Spectral 3D Image Compression

Maugey, Thomas; Rizkallah, Mira; Bidgoli, Navid Mahmoudian; Roumy, Aline; Guillemot, Christine

doi:10.1002/9781119850830.ch5

Cited by 1 publication

(1 citation statement)

References 57 publications

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“…To jointly account for this structure in both data and functions, we draw from the powerful literature on characteristic graphs, introduced by Körner for source coding [ 75 ] and used in data compression [ 62 , 73 , 74 , 76 , 77 , 78 ], cryptography [ 79 ], image processing [ 80 ], and bioinformatics [ 81 ]. For example, toward understanding the fundamental limits of distributed functional compression, the work in [ 75 ] devised the graph entropy approach in order to provide the best possible encoding rate of an information source with vanishing error probability.…”

Section: Introductionmentioning

confidence: 99%

Multi-Server Multi-Function Distributed Computation

Malak,

Deylam Salehi,

Serbetci

et al. 2024

Entropy

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The work here studies the communication cost for a multi-server multi-task distributed computation framework, as well as for a broad class of functions and data statistics. Considering the framework where a user seeks the computation of multiple complex (conceivably non-linear) tasks from a set of distributed servers, we establish the communication cost upper bounds for a variety of data statistics, function classes, and data placements across the servers. To do so, we proceed to apply, for the first time here, Körner’s characteristic graph approach—which is known to capture the structural properties of data and functions—to the promising framework of multi-server multi-task distributed computing. Going beyond the general expressions, and in order to offer clearer insight, we also consider the well-known scenario of cyclic dataset placement and linearly separable functions over the binary field, in which case, our approach exhibits considerable gains over the state of the art. Similar gains are identified for the case of multi-linear functions.

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