Intelligent DRL-Based Adaptive Region of Interest for Delay-sensitive Telemedicine Applications
This provides an incremental improvement for telemedicine applications by reducing latency to enhance remote surgical and knowledge transfer experiences.
The paper tackles the problem of video streaming delay in telemedicine by proposing a Deep Reinforcement Learning model that adapts region of interest size and quality based on throughput, resulting in a 13% reduction in delay while maintaining acceptable quality.
Telemedicine applications have recently received substantial potential and interest, especially after the COVID-19 pandemic. Remote experience will help people get their complex surgery done or transfer knowledge to local surgeons, without the need to travel abroad. Even with breakthrough improvements in internet speeds, the delay in video streaming is still a hurdle in telemedicine applications. This imposes using image compression and region of interest (ROI) techniques to reduce the data size and transmission needs. This paper proposes a Deep Reinforcement Learning (DRL) model that intelligently adapts the ROI size and non-ROI quality depending on the estimated throughput. The delay and structural similarity index measure (SSIM) comparison are used to assess the DRL model. The comparison findings and the practical application reveal that DRL is capable of reducing the delay by 13% and keeping the overall quality in an acceptable range. Since the latency has been significantly reduced, these findings are a valuable enhancement to telemedicine applications.