With the rapid growth of video data traffic on the Internet and the development of new types of video transmission systems, the need for ad hoc video encoders has also increased. One such case involves Unmanned Aerial Vehicles (UAVs), widely known as drones, which are used in drone races, search and rescue efforts, capturing panoramic views, and so on. In this paper, we provide an efficiency comparison of the two most popular video encoders—H.264 and H.265—in a drone piloting system using first-person view (FPV). In this system, a drone is used to capture video, which is then transmitted to FPV goggles in real time. We examine the compression efficiency of 4K drone footage by varying parameters such as Group of Pictures (GOP) size, Quantization Parameter (QP), and target bitrate. The quality of the compressed footage is determined using four objective video quality measures: PSNR, SSIM, VMAF, and BRISQUE. Apart from video quality, encoding time and encoding energy consumption are also compared. The research was performed using numerous nodes on a supercomputer.

Unmanned Aerial Vehicles (UAVs) are increasingly being used in a variety of applications, including entertainment, surveillance, and delivery. However, the real-time Motion Estimation (ME) of UAVs is challenging due to the high speed and unpredictable movements of these vehicles. This paper presents a novel algorithm for optimizing ME for Remotely Controlled Vehicles (RCVs), with a particular focus on UAVs. The proposed algorithm, called Motion Dynamics Input Search (MDIS), incorporates information from vehicle motion dynamics estimation to enhance the accuracy and efficiency of ME. The MDIS algorithm addresses the challenges associated with real-time ME in RCVs by leveraging user input to guide the search for the most similar blocks in the previous video frame. Through extensive experimentation and evaluation, this study demonstrates the effectiveness of the proposed algorithm in improving ME performance for RCVs. The findings highlight the potential impact of user interaction and motion dynamics estimation in shaping the future of ME algorithms for RCVs and similar applications.

In the pursuit of effective real-time video transmission for First-Person View (FPV) drone systems, optimizing the encoding process is paramount. Traditional encoding methods, reliant on pre-encoding demosaicking, often fall short in balancing the trade-off between video quality and latency, essential for seamless real-time feedback. This work proposes a novel approach by deferring the demosaicking process to the decoder side, thereby encoding the rearranged Bayer pattern (RGGB) data directly. This deferment significantly reduces the input data size, to the tune of a threefold reduction, thereby achieving a more expeditious encoding process. The tailored encoder and decoder architecture ensures the accurate reconstruction of the full-color image on the decoder side. Through a comprehensive evaluation, leveraging a specialized video quality assessment framework designed for FPV drone footage, our findings illuminate the substantial benefits of our proposed method. Specifically, it achieves faster encoding times and reduced computational overhead, pivotal for low-latency applications. Furthermore, this study opens avenues for integrating advanced encoding techniques into commercial FPV drone systems, potentially enriching user experiences across various applications. Our research not only addresses a critical gap in real-time video transmission but also sets the stage for future exploration into optimizing encoding methodologies for the next generation of FPV drone technologies.

Real-time video coding on UAV devices presents a challenge where all components of the system must be optimised to get the lowest latency in the video transmission. We focus on the coding and decoding part of the pipeline, where the coding side is located on the UAV and needs to also be energy efficient. Choosing the right number of reference frames which are looked at when searching for a reference block in the motion compensation in High Efficiency Video Coding (HEVC) is crucial for getting the requested video quality and maintaining the latency and power consumption in the requested range. By increasing the number of searched reference frames, the quality increases, but as a drawback, the power consumption and latency also increase. We investigated the impact of the Group of Pictures (GOP) structure and the selection of the Motion Estimation (ME) algorithm on the quality of 4K drone footage encoded using the Kvazaar implementation of the HEVC standard. We vary different encoding parameters such as ME algorithm, ME search step, number of referent frames used in ME, target bitrate, etc. After encoding, three objective quality measures were calculated for all the videos. Since there were over 4000 examined videos, everything was done remotely on a supercomputer BURA.