1,542 research outputs found
Quality-Gated Convolutional LSTM for Enhancing Compressed Video
The past decade has witnessed great success in applying deep learning to
enhance the quality of compressed video. However, the existing approaches aim
at quality enhancement on a single frame, or only using fixed neighboring
frames. Thus they fail to take full advantage of the inter-frame correlation in
the video. This paper proposes the Quality-Gated Convolutional Long Short-Term
Memory (QG-ConvLSTM) network with bi-directional recurrent structure to fully
exploit the advantageous information in a large range of frames. More
importantly, due to the obvious quality fluctuation among compressed frames,
higher quality frames can provide more useful information for other frames to
enhance quality. Therefore, we propose learning the "forget" and "input" gates
in the ConvLSTM cell from quality-related features. As such, the frames with
various quality contribute to the memory in ConvLSTM with different importance,
making the information of each frame reasonably and adequately used. Finally,
the experiments validate the effectiveness of our QG-ConvLSTM approach in
advancing the state-of-the-art quality enhancement of compressed video, and the
ablation study shows that our QG-ConvLSTM approach is learnt to make a
trade-off between quality and correlation when leveraging multi-frame
information. The project page: https://github.com/ryangchn/QG-ConvLSTM.git.Comment: Accepted to IEEE International Conference on Multimedia and Expo
(ICME) 201
Spatial second-order positive and asymptotic preserving filtered schemes for nonlinear radiative transfer equations
A spatial second-order scheme for the nonlinear radiative transfer equations
is introduced in this paper. The discretization scheme is based on the filtered
spherical harmonics () method for the angular variable and the unified
gas kinetic scheme (UGKS) framework for the spatial and temporal variables
respectively. In order to keep the scheme positive and second-order accuracy,
firstly, we use the implicit Monte Carlo linearization method [6] in the
construction of the UGKS numerical boundary fluxes. Then, by carefully
analyzing the constructed second-order fluxes involved in the macro-micro
decomposition, which is induced by the angular discretization, we
establish the sufficient conditions that guarantee the positivity of the
radiative energy density and material temperature. Finally, we employ linear
scaling limiters for the angular variable in the reconstruction and for
the spatial variable in the piecewise linear slopes reconstruction
respectively, which are shown to be realizable and reasonable to enforce the
sufficient conditions holding. Thus, the desired scheme, called the
-based UGKS, is obtained. Furthermore, in the regime
and the regime , a simplified spatial second-order scheme,
called the -based SUGKS, is presented, which possesses all the
properties of the non-simplified one. Inheriting the merit of UGKS, the
proposed schemes are asymptotic preserving. By employing the method for
the angular variable, the proposed schemes are almost free of ray effects. To
our best knowledge, this is the first time that spatial second-order, positive,
asymptotic preserving and almost free of ray effects schemes are constructed
for the nonlinear radiative transfer equations without operator splitting.
Various numerical experiments are included to validate the properties of the
proposed schemes
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