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.

移动设备上的视频流媒体体验优化是一个挑战，本文提出了一种基于深度学习的实时增强方法，包括视频帧恢复、超分辨率算法和接收端增强感知的视频比特率自适应算法，评估结果显示该方法能够提高视频流媒体系统的实时增强效果，并显著提高 24% 至 82% 的视频体验质量。

移动设备上的实时神经视频恢复与增强

Real-Time Neural Video Recovery and Enhancement on Mobile Devices

Accurate lane detection is critical for navigation in autonomous vehicles,
particularly the active lane which demarcates the single road space that the
vehicle is currently traveling on. Recent state-of-the-art lane detection
algorithms utilize convolutional neural networks (CNNs) to train deep learning
models on popular benchmarks such as TuSimple and CULane. While each of these
models works particularly well on train and test inputs obtained from the same
dataset, the performance drops significantly on unseen datasets of different
environments. In this paper, we present a real-time robust neural network
output enhancement for active lane detection (RONELD) method to identify,
track, and optimize active lanes from deep learning probability map outputs. We
first adaptively extract lane points from the probability map outputs, followed
by detecting curved and straight lanes before using weighted least squares
linear regression on straight lanes to fix broken lane edges resulting from
fragmentation of edge maps in real images. Lastly, we hypothesize true active
lanes through tracking preceding frames. Experimental results demonstrate an up
to two-fold increase in accuracy using RONELD on cross-dataset validation
tests.

本文介绍了一种实时的、鲁棒的神经网络输出增强方法 (RONELD)，用于从深度学习的概率地图输出中识别、跟踪和优化行驶中的车道，并在跨数据集验证测试中展示出多达两倍的精度提升。