Online Video Streaming Super-Resolution with Adaptive Look-Up Table Fusion

This paper focuses on Super-resolution for online video streaming data. Applying existing super-resolution methods to video streaming data is non-trivial for two reasons. First, to support application with constant interactions, video streaming has a high requirement for latency that most existing methods are less applicable, especially on low-end devices. Second, existing video streaming protocols (e.g., WebRTC) dynamically adapt the video quality to the network condition, thus video streaming in the wild varies greatly under different network bandwidths, which leads to diverse and dynamic degradations. To tackle the above two challenges, we proposed a novel video super-resolution method for online video streaming. First, we incorporate Look-Up Table (LUT) to lightweight convolution modules to achieve real-time latency. Second, for variant degradations, we propose a pixel-level LUT fusion strategy, where a set of LUT bases are built upon state-of-the-art SR networks pre-trained on different degraded data, and those LUT bases are combined with extracted weights from lightweight convolution modules to adaptively handle dynamic degradations. Extensive experiments are conducted on a newly proposed online video streaming dataset named LDV-WebRTC. All the results show that our method significantly outperforms existing LUT-based methods and offers competitive SR performance with faster speed compared to efficient CNN-based methods. Accelerated with our parallel LUT inference, our proposed method can even support online 720P video SR around 100 FPS.

Enhance the performance of navigation: A two-stage machine learning approach

Real time traffic navigation is an important capability in smart transportation technologies, which has been extensively studied these years. Due to the vast development of edge devices, collecting real time traffic data is no longer a problem. However, real traffic navigation is still considered to be a particularly challenging problem because of the time-varying patterns of the traffic flow and unpredictable accidents/congestion. To give accurate and reliable navigation results, predicting the future traffic flow(speed,congestion,volume,etc) in a fast and accurate way is of great importance. In this paper, we adopt the ideas of ensemble learning and develop a two-stage machine learning model to give accurate navigation results. We model the traffic flow as a time series and apply XGBoost algorithm to get accurate predictions on future traffic conditions(1st stage). We then apply the Top K Dijkstra algorithm to find a set of shortest paths from the give start point to the destination as the candidates of the output optimal path. With the prediction results in the 1st stage, we find one optimal path from the candidates as the output of the navigation algorithm. We show that our navigation algorithm can be greatly improved via EOPF(Enhanced Optimal Path Finding), which is based on neural network(2nd stage). We show that our method can be over 7% better than the method without EOPF in many situations, which indicates the effectiveness of our model.