## 4 Experimental Results  
Over 70 test cases, encompassing all types of movements, were carried out using footage from a simulator and real drone footage. Pre-recorded data were used for verifying the algorithm to ensure consistent and repeatable testing conditions. Moreover, pre-recorded data are in the highest-quality raw format so that there are no coding artifacts in the input video. This allows us to thoroughly evaluate the algorithm's performance under controlled scenarios, using both simulator footage with precise user input data and real drone footage. By doing so, we can accurately measure and compare the performance of the MDIS algorithm against the DS algorithm without the variability that might arise from live data collection. The simulator sequence under test spans 46 s and is presented in a 4K resolution at 60 frames per second. Real drone footage consists of 4 short sequences (4-20 s per sequence), also in 4K resolution, at 60 frames per second. The tests were performed on a 64-bit Windows personal computer with 16 GB of RAM, 11 th Gen Intel(R) Core(TM) i5-11400F @ 2.60 GHz processor (Intel, Santa Clara, CA, USA), and an NVIDIA GeForce GTX 1650 graphics card (NVIDIA, Santa Clara, CA, USA). The simulator footage was given particular attention because it provided precise user input data from the controller that corresponded to the footage. For the real drone footage, we manually created the textual user input files. This was done by individually inspecting each sequence and tracking the drone movements. Nevertheless, the approach of manually creating user input files for the real drone footage is also valid, because the data from the controller undergo the same processing as the approximated manual input, ensuring a consistent methodology across both types of footage. In order to precisely measure the encoding times and times occupied by each function separately, Intel's VTune Profiler software version 2023.0.0 was used [25]. The Flame Graph analysis was used the most, since it shows exactly how much CPU time each function occupied. In order to optimize each search model independently, the simulator footage was segmented into several shorter sequences, each approximately 300 frames long. Each of these sequences was characterized by a single specific type of movement, namely left-right rotation, forwards-backwards motion, left-right motion, or upwards-downwards motion. Several versions of MDIS were tested, and the following results refer to the best version, which will be explained in detail. Figure 15 presents a comparison chart illustrating CPU time consumption, derived from Flame graph analysis, for encoding the entire simulator sequence using the MDIS and DS algorithms. The bars on the left side of the chart represent the CPU time used exclusively for the search algorithm functions. In contrast, the bars on the right side illustrate the CPU time consumed by the broader _search_ref_ function, which includes not only the search functions but also other operations like early termination and selecting starting points, which are key elements of the whole ME process. This comparison shows that MDIS speeds up the overall ME process and does  
In addition to accelerating the encoding speed, the MDIS algorithm also maintains the same video quality as the DS algorithm, as evidenced by PSNR values which were consistently within a \(\pm\)0.6% range. Furthermore, MDIS offers a superior compression ratio. While the video encoded with DS amounts to 264 MB, the MDIS-encoded video is smaller, with a size of 257 MB. Table 2 represents encoding data for real drone footage. Encoded video file size refers to the final video file size of the corresponding real drone sequence, expressed in MB. Search time refers to the CPU time consumed solely by the search function--by MDIS or DS. PSNR is calculated using FFmpeg [26] by comparing the final encoded video and the original, raw video.