171 research outputs found
Real-Time Neural Video Recovery and Enhancement on Mobile Devices
As mobile devices become increasingly popular for video streaming, it's
crucial to optimize the streaming experience for these devices. Although deep
learning-based video enhancement techniques are gaining attention, most of them
cannot support real-time enhancement on mobile devices. Additionally, many of
these techniques are focused solely on super-resolution and cannot handle
partial or complete loss or corruption of video frames, which is common on the
Internet and wireless networks.
To overcome these challenges, we present a novel approach in this paper. Our
approach consists of (i) a novel video frame recovery scheme, (ii) a new
super-resolution algorithm, and (iii) a receiver enhancement-aware video bit
rate adaptation algorithm. We have implemented our approach on an iPhone 12,
and it can support 30 frames per second (FPS). We have evaluated our approach
in various networks such as WiFi, 3G, 4G, and 5G networks. Our evaluation shows
that our approach enables real-time enhancement and results in a significant
increase in video QoE (Quality of Experience) of 24\% - 82\% in our video
streaming system
Neural Video Recovery for Cloud Gaming
Cloud gaming is a multi-billion dollar industry. A client in cloud gaming
sends its movement to the game server on the Internet, which renders and
transmits the resulting video back. In order to provide a good gaming
experience, a latency below 80 ms is required. This means that video rendering,
encoding, transmission, decoding, and display have to finish within that time
frame, which is especially challenging to achieve due to server overload,
network congestion, and losses. In this paper, we propose a new method for
recovering lost or corrupted video frames in cloud gaming. Unlike traditional
video frame recovery, our approach uses game states to significantly enhance
recovery accuracy and utilizes partially decoded frames to recover lost
portions. We develop a holistic system that consists of (i) efficiently
extracting game states, (ii) modifying H.264 video decoder to generate a mask
to indicate which portions of video frames need recovery, and (iii) designing a
novel neural network to recover either complete or partial video frames. Our
approach is extensively evaluated using iPhone 12 and laptop implementations,
and we demonstrate the utility of game states in the game video recovery and
the effectiveness of our overall design
Advanced NOMA Assisted Semi-Grant-Free Transmission Schemes for Randomly Distributed Users
Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF)
transmission has recently received significant research attention due to its
outstanding ability of serving grant-free (GF) users with grant-based (GB)
users' spectrum, {\color{blue}which can greatly improve the spectrum efficiency
and effectively relieve the massive access problem of 5G and beyond networks.
In this paper, we investigate the performance of SGF schemes under more
practical settings.} Firstly, we study the outage performance of the best user
scheduling SGF scheme (BU-SGF) by considering the impacts of Rayleigh fading,
path loss, and random user locations. Then, a fair SGF scheme is proposed by
applying cumulative distribution function (CDF)-based scheduling (CS-SGF),
which can also make full use of multi-user diversity. Moreover, by employing
the theories of order statistics and stochastic geometry, we analyze the outage
performances of both BU-SGF and CS-SGF schemes. Results show that full
diversity orders can be achieved only when the served users' data rate is
capped, which severely limit the rate performance of SGF schemes. To further
address this issue, we propose a distributed power control strategy to relax
such data rate constraint, and derive closed-form expressions of the two
schemes' outage performances under this strategy. Finally, simulation results
validate the fairness performance of the proposed CS-SGF scheme, the
effectiveness of the power control strategy, and the accuracy of the
theoretical analyses.Comment: 41 pages, 8 figure
Transferable Attack for Semantic Segmentation
We analysis performance of semantic segmentation models wrt. adversarial
attacks, and observe that the adversarial examples generated from a source
model fail to attack the target models. i.e The conventional attack methods,
such as PGD and FGSM, do not transfer well to target models, making it
necessary to study the transferable attacks, especially transferable attacks
for semantic segmentation. We find two main factors to achieve transferable
attack. Firstly, the attack should come with effective data augmentation and
translation-invariant features to deal with unseen models. Secondly, stabilized
optimization strategies are needed to find the optimal attack direction. Based
on the above observations, we propose an ensemble attack for semantic
segmentation to achieve more effective attacks with higher transferability. The
source code and experimental results are publicly available via our project
page: https://github.com/anucvers/TASS.Comment: Source code is available at: https://github.com/anucvers/TAS
A modified ant colony optimization algorithm for network coding resource minimization
The paper presents a modified ant colony optimization approach for the network coding resource minimization problem. It is featured with several attractive mechanisms specially devised for solving the network coding resource minimization problem: 1) a multi-dimensional pheromone maintenance mechanism is put forward to address the issue of pheromone overlapping; 2) problem-specific heuristic information is employed to enhance the heuristic search (neighboring area search) capability; 3) a tabu-table based path construction method is devised to facilitate the construction of feasible (link-disjoint) paths from the source to each receiver; 4) a local pheromone updating rule is developed to guide ants to construct appropriate promising paths; 5) a solution reconstruction method is presented, with the aim of avoiding prematurity and improving the global search efficiency of proposed algorithm. Due to the way it works, the ant colony optimization can well exploit the global and local information of routing related problems during the solution construction phase. The simulation results on benchmark instances demonstrate that with the five extended mechanisms integrated, our algorithm outperforms a number of existing algorithms with respect to the best solutions obtained and the computational time
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