No abstract

An adaptive algorithm for extracting foreground objects from background in videophone or videoconference applications is presented in this paper. The algorithm uses a neural network architecture that classifies the video frames in regionsof-interest (ROI) and non-ROI areas, also being able to automatically adapt its performance to scene changes. The algorithm is incorporated in motion-compensated discrete cosine transform (MC-DCT)-based coding schemes, allocating more bits to ROI than to non-ROI areas. Simulation results are presented, using the Claire and Trevor sequences, which show reconstructed images of better quality, as well as signal-to-noise ratio improvements of about 1.4 dB, compared to those achieved by standard MC-DCT encoders.

In this paper, we focused on two prevailing architectural approaches for control-plane virtualization in multi-tenant OpenFlow-ready SDN domains: The first permits the delegation of a specific, non-overlapping part of the overall flowspace to each tenant OpenFlow controller, exposing him/her the entire substrate topology; the second conceals the substrate topology to tenants by abstracting resources and exposing usercontrolled (tenant) Virtual Networks (VNs). For both cases, we propose and analyze three control-plane slicing methods (domain, switch and port-wide), enforced by the management plane, that safeguard control-plane isolation among tenant VNs. Their effectiveness is assessed in terms of control-plane resources (number of flowspace policy rule entries, table lookup times and memory consumption) via measurements on a prototype implementation. To that end, we introduced and prototyped the Flowspace Slicing Policy (FSP) rule engine, an automated mechanism translating substrate management-plane policies into VN mapping control-plane rules. Our experiments, involving thousands of tenants VN requests over a variety of WAN-scalenetwork topologies (e.g. Internet2/OSE3 and GÉANT), demonstrate that the port-wide slicing method is the most efficient in terms of tenant request acceptance ratio, within acceptable control-plane delays and memory consumption.

No abstract

No abstract