Recent rapid advancements in intelligent vehicular systems and deep learning techniques have led to the emergence of diverse applications utilizing high-quality automotive videos in the Internet-of-Vehicles (IoV), often assisted by uncrewed aerial vehicles (UAVs). These applications aim to provide convenience and security for users. However, transmitting automotive videos with high-quality and low-bit-rate poses a challenge due to the inherent lossiness of traditional compression codecs in current UAV-assisted IoV systems, thereby affecting the performance of subsequent tasks. To address this, we propose a spatial-temporal hybrid video compression framework (STHVC), which integrates Space-Time Super-Resolution (STSR) with conventional codecs to enhance the compression efficiency on automotive videos. In our hybrid design, the encoder generates a low-frame-rate and low-resolution version of the source video, which is then compressed using a traditional codec. During the decoding stage, an effective STSR network is developed to increase both the resolution and the frame rate, and mitigate compression artifacts for automotive videos simultaneously. Additionally, we introduce a rectified intermediate flow estimation technique (RecIFE) within the proposed STSR network to address the challenge of noisy and inaccurate motions during the compression pipeline. Extensive experiments on various benchmark datasets demonstrate that our approach achieves bit-rate reductions of 29.97% compared to H.265 (slow) and 31.27% compared to H.266, while also exhibiting superior restoration performance compared to other state-of-the-art learning-based approaches.

With the development of embedded systems and deep learning, it is feasible to combine them for offering various and convenient human-centered services, which is based on high-quality (HQ) videos. However, due to the limit of video traffic load and unavoidable noise, the visual quality of an image from an edge camera may degrade significantly, influencing the overall video and service quality. To maintain video stability, video quality enhancement (QE), aiming at recovering HQ videos from their distorted low-quality (LQ) sources, has aroused increasing attention in recent years. The key challenge for video QE lies in how to effectively aggregate complementary information from multiple frames (i.e., temporal fusion). To handle diverse motion in videos, existing methods commonly apply motion compensation before the temporal fusion. However, the motion field estimated from the distorted LQ video tends to be inaccurate and unreliable, thereby resulting in ineffective fusion and restoration. In addition, motion estimation for consecutive frames is generally conducted in a pairwise manner, which leads to expensive and inefficient computation. In this article, we propose a fast yet effective temporal fusion scheme for video QE by incorporating a novel Spatio-Temporal Deformable Convolution (STDC) to simultaneously compensate motion and aggregate temporal information. Specifically, the proposed temporal fusion scheme takes a target frame along with its adjacent reference frames as input to jointly estimate an offset field to deform the spatio-temporal sampling positions of convolution. As a result, complementary information from multiple frames can be fused within the STDC operation in one forward pass. Extensive experimental results on three benchmark datasets show that our method performs favorably to the state of the art in terms of accuracy and efficiency.

Recent years have witnessed remarkable success of deep learning methods in quality enhancement for compressed video. To better explore temporal information, existing methods usually estimate optical flow for temporal motion compensation. However, since compressed video could be seriously distorted by various compression artifacts, the estimated optical flow tends to be inaccurate and unreliable, thereby resulting in ineffective quality enhancement. In addition, optical flow estimation for consecutive frames is generally conducted in a pairwise manner, which is computational expensive and inefficient. In this paper, we propose a fast yet effective method for compressed video quality enhancement by incorporating a novel Spatio-Temporal Deformable Fusion (STDF) scheme to aggregate temporal information. Specifically, the proposed STDF takes a target frame along with its neighboring reference frames as input to jointly predict an offset field to deform the spatio-temporal sampling positions of convolution. As a result, complementary information from both target and reference frames can be fused within a single Spatio-Temporal Deformable Convolution (STDC) operation. Extensive experiments show that our method achieves the state-of-the-art performance of compressed video quality enhancement in terms of both accuracy and efficiency.

The recent technology advancement on unmanned aerial vehicle (UAV) has enabled diverse applications in vision-related outdoor tasks. Visual object detection is a crucial task among them. However, it is difficult to actually deploy detectors on embedded devices due to the challenges among energy consumption, accuracy, and speed. In this article, we address a few key challenges from the platform, application to the system, and propose an energy-efficient system for real-time UAV object detection on an embedded platform. The proposed system can achieve speed of 28.5 FPS and 2.7-FPS/W energy efficiency on the data set from 2018 low-power object detection challenges (LPODCs).