Rate-Distortion Performance of Sequential Massive Random Access to Gaussian Sources with Memory

Elsa Dupraz 1, 2 Thomas Maugey 3 Aline Roumy 3 Michel Kieffer 4
1 Lab-STICC_IMTA_CACS_COM
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
3 Sirocco - Analysis representation, compression and communication of visual data
Inria Rennes – Bretagne Atlantique , IRISA_D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : In Sequential Massive Random Access (SMRA), a set of correlated sources is jointly encoded and stored on a server, and clients want to access to only a subset of the sources. Since the number of simultaneous clients can be huge, the server is only authorized to extract a bitstream from the stored data: no re-encoding can be performed before the transmission of a request. In this paper, we investigate the SMRA performance of lossy source coding of Gaussian sources with memory. In practical applications such as Free Viewpoint Television, this model permits to take into account not only inter but also intra correlation between sources. For this model, we provide the storage and transmission rates that are achievable for SMRA under some distortion constraint, and we consider two particular examples of Gaussian sources with memory.
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Elsa Dupraz, Thomas Maugey, Aline Roumy, Michel Kieffer. Rate-Distortion Performance of Sequential Massive Random Access to Gaussian Sources with Memory. DCC 2018 - Data compression conference, Mar 2018, Snowbird, United States. pp.1-10, ⟨10.1109/dcc.2018.00059 ⟩. ⟨hal-01759076⟩

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