# Performance Comparison of H.264 and H.265 Encoders in a 4K FPV Drone Piloting System
## 5 Conclusions and Future Work  
In this paper, we provided an efficiency comparison overview of the two most popular video encoders--x264 and x265--for use in UAV systems. We devised a test environment on HPC BURA and successfully obtained and analyzed compressed footage data. After filming 16 drone sequences, we encoded them while varying the GOP size, min and max QP, and target bitrate. For each encoded sequence, we calculated four objective quality measures and tracked the encoding time. The overall results confirmed our assumptions and indicated that libx265 dominated over the libx264 codec in every tested aspect, except for encoding time.  
In all test sequences, x265 outperformed x264 by up to 10% in PSNR quality and up to 5% in SSIM quality. With 4K footage compressed up to 10 Mbps rates, BRISQUE scores demonstrated that the quality with x265 was up to 11% better than that with x264; furthermore, for VMAF, x265 outperformed x264 by up to 20%.  
All four quality metrics (PSNR, SSIM, BRISQUE, and VMAF) showed that the quality of drone video encoded at the same bitrate with x265 was better than that of x264. Encoding times for the two encoders showed that x265 requires higher processing power: up to 51%, on average, depending on the encoding parameters. This means that x264 might be a better pick than x265 when parameters such as battery capacity or encoding latency are of the essence.  
x265 showed compression quality and superiority especially at lower bitrates while, for the highest bitrates, when the compression factors were lower, both codecs produced compressed videos with no significant difference in quality.  
Classification of the sequences showed differences in the bitrate, depending on the temporal and spatial complexity of the video. A higher spatial complexity of the video introduced an additional increase in the size of the encoded video at the same target video quality. This can be used when setting the parameters for the encoder and estimating the output bitrate based on the type of video scene.  
Future research could include a comparison of wavelet-based video codecs, which have gained a lot of attention lately. Sequence classification--or, more precisely, the spatial and temporal complexity--could be approximated using mathematical models.  
Conceptualization, [PERSON]; methodology, J.B. and D.H.; software, J.B.; validation, J.B. and D.H.; formal analysis, J.B. and D.H.; investigation, J.B.; resources, D.H.; data curation, J.B.; writing--original draft preparation, J.B., D.H., J.K. and M.Z.; writing--review and editing, J.B., D.H., J.K. and M.Z.; visualization, J.B. and D.H.; supervision, D.H.; project administration, D.H.; funding acquisition, D.H. All authors have read and agreed to the published version of the manuscript.  
This work has been supported in part by the project KK.01.2.1.02.0054 Razvoj uredaja za prijenos video signala ultra niske latencje (Development of ultra low latency video signal transmission device), financed by the EU from the European Regional Development Fund.  
**Acknowledgments:** The authors would like to thank [PERSON] for providing the DJI ZENMUSE XSS camera and DJI Inspire 2 drone for the experiments carried out in this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.