96,127 research outputs found
An efficient rate control algorithm for a wavelet video codec
Rate control plays an essential role in video coding and transmission to provide the best video quality at the receiver's end given the constraint of certain network conditions. In this paper, a rate control algorithm using the Quality Factor (QF) optimization method is proposed for the wavelet-based video codec and implemented on an open source Dirac video encoder. A mathematical model which we call Rate-QF (R - QF) model is derived to generate the optimum QF for the current coding frame according to the target bitrate. The proposed algorithm is a complete one pass process and does not require complex mathematical calculation. The process of calculating the QF is quite simple and further calculation is not required for each coded frame. The experimental results show that the proposed algorithm can control the bitrate precisely (within 1% of target bitrate in average). Moreover, the variation of bitrate over each Group of Pictures (GOPs) is lower than that of H.264. This is an advantage in preventing the buffer overflow and underflow for real-time multimedia data streaming
New Lepton Family Symmetry and Neutrino Tribimaximal Mixing
The newly proposed finite symmetry Sigma(81) is applied to the problem of
neutrino tribimaximal mixing. The result is more satisfactory than those of
previous models based on A_4 in that the use of auxiliary symmetries (or
mechanisms) may be avoided. Deviations from the tribimaximal pattern are
expected, but because of its basic structure, only tan^2 (theta_12) may differ
significantly from 0.5 (say 0.45) with sin^2 (2 theta_23) remaining very close
to one, and theta_13 very nearly zero.Comment: 8 pages, no figur
Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
Learning deeper convolutional neural networks becomes a tendency in recent
years. However, many empirical evidences suggest that performance improvement
cannot be gained by simply stacking more layers. In this paper, we consider the
issue from an information theoretical perspective, and propose a novel method
Relay Backpropagation, that encourages the propagation of effective information
through the network in training stage. By virtue of the method, we achieved the
first place in ILSVRC 2015 Scene Classification Challenge. Extensive
experiments on two challenging large scale datasets demonstrate the
effectiveness of our method is not restricted to a specific dataset or network
architecture. Our models will be available to the research community later.Comment: Technical report for our submissions to the ILSVRC 2015 Scene
Classification Challenge, where we won the first plac
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