43,576 research outputs found
Why (and How) Networks Should Run Themselves
The proliferation of networked devices, systems, and applications that we
depend on every day makes managing networks more important than ever. The
increasing security, availability, and performance demands of these
applications suggest that these increasingly difficult network management
problems be solved in real time, across a complex web of interacting protocols
and systems. Alas, just as the importance of network management has increased,
the network has grown so complex that it is seemingly unmanageable. In this new
era, network management requires a fundamentally new approach. Instead of
optimizations based on closed-form analysis of individual protocols, network
operators need data-driven, machine-learning-based models of end-to-end and
application performance based on high-level policy goals and a holistic view of
the underlying components. Instead of anomaly detection algorithms that operate
on offline analysis of network traces, operators need classification and
detection algorithms that can make real-time, closed-loop decisions. Networks
should learn to drive themselves. This paper explores this concept, discussing
how we might attain this ambitious goal by more closely coupling measurement
with real-time control and by relying on learning for inference and prediction
about a networked application or system, as opposed to closed-form analysis of
individual protocols
No-reference bitstream-based impairment detection for high efficiency video coding
Video distribution over error-prone Internet Protocol (IP) networks results in visual impairments on the received video streams. Objective impairment detection algorithms are crucial for maintaining a high Quality of Experience (QoE) as provided with IPTV distribution. There is a lot of research invested in H.264/AVC impairment detection models and questions rise if these turn obsolete with a transition to the successor of H.264/AVC, called High Efficiency Video Coding (HEVC). In this paper, first we show that impairments on HEVC compressed sequences are more visible compaired to H.264/AVC encoded sequences. We also show that an impairment detection model designed for H.264/AVC could be reused on HEVC, but that caution is advised. A more accurate model taking into account content classification needed slight modification to remain applicable for HEVC compression video content
Geometry-Based Next Frame Prediction from Monocular Video
We consider the problem of next frame prediction from video input. A
recurrent convolutional neural network is trained to predict depth from
monocular video input, which, along with the current video image and the camera
trajectory, can then be used to compute the next frame. Unlike prior next-frame
prediction approaches, we take advantage of the scene geometry and use the
predicted depth for generating the next frame prediction. Our approach can
produce rich next frame predictions which include depth information attached to
each pixel. Another novel aspect of our approach is that it predicts depth from
a sequence of images (e.g. in a video), rather than from a single still image.
We evaluate the proposed approach on the KITTI dataset, a standard dataset for
benchmarking tasks relevant to autonomous driving. The proposed method produces
results which are visually and numerically superior to existing methods that
directly predict the next frame. We show that the accuracy of depth prediction
improves as more prior frames are considered.Comment: To appear in 2017 IEEE Intelligent Vehicles Symposiu
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