## 1 Introduction  
ME is a critical component in many video processing applications, such as video compression, object tracking, and video stabilization. ME works by predicting the movement of objects within video frames, allowing for more efficient data encoding and transmission. Common ME algorithms include Full Search (FS), Diamond Search (DS), Hexagonal Search (HS) and Test-Zone Search (TZS), each offering a different trade-off between computational complexity and accuracy. FS, while the most accurate, is computationally intensive, making it less suitable for real-time applications. DS, HS, and TZS, on the other hand, provide faster computations with slightly lower accuracy, making them more practical for applications requiring low latency.  
In recent years, there has been growing interest in effective video compression for RCVs, such as drones, and UAVs [1]. This is due to the increasing popularity of UAVs for applications such as aerial photography, videography, military, and entertainment [2]. The entertainment sector has witnessed a significant boost with the rise of drone racing. This engaging sport involves pilots skillfully maneuvering UAVs through obstacle courses, aiming to complete the course in the quickest time possible. As drones can fly very fast when it comes to drone racing, it is crucial that the latency in video transmission from the drone to the pilot's first-person view (FPV) goggles is as minimal as possible. This is not only important for the pilot's performance, but also for the safety of the drone, as real-time video adaptation can help avoid collisions [3]. Statistics indicate a growing need for low-latency video processing in drones and other technologies. For instance, drone racing requires video transmission latencies below 150 milliseconds to ensure that pilots can react swiftly to their environment. Similarly, applications in military surveillance and emergency response demand near-instantaneous video feedback to make timely and informed decisions.  
To address this, our research focuses on reducing latency through video compression before network transmission, a challenging task under limited computing resources. Since ME is typically the most time-consuming part of video coding, we propose an innovative approach to accelerate it. This paper introduces a novel ME algorithm, Motion Dynamics Input Search (MDIS), which builds upon the foundation of the well-known DS algorithm while incorporating motion dynamics estimation that responds to real-time user interaction with the controller. Instead of searching all the points of the diamond for each block, MDIS only searches the selected locations based on the user input from the controller. Additionally, the search range is modified based on the location of the pixel in the image and the current drone movement type. While our research is centered on UAVs and FPV systems, the MDIS algorithm can be applied to any RCV system.  
We evaluate MDIS on a drone-flying simulator which records user inputs for each frame. We also evaluate it on real 4K drone footage. By leveraging motion dynamics estimation in the ME process, our algorithm aims to enhance the efficiency, accuracy, and adaptability of ME for RCVs, with a primary focus on UAVs.  
In summary, the main contributions of this paper are as follows:  
* The development of the MDIS algorithm, which integrates motion dynamics estimation and user interaction to enhance ME efficiency.
* The comprehensive evaluation of MDIS using both simulated and real drone footage, demonstrating significant improvements in encoding speed and compression ratio without compromising video quality.
* The implementation and testing of MDIS within an open-source HEVC video encoder, showcasing its practical applicability and performance advantages.