Adaptive Bitrate Selection for Medical Video Compression Balancing Bandwidth Efficiency and Segmentation Quality

Medical video compression is essential for reducing storage and bandwidth requirements while ensuring the preservation of diagnostic and segmentation quality, enabling efficient transmission and analysis in healthcare applications. The primary challenge in medical video compression lies in achieving significant data reduction without compromising critical visual details required for accurate diagnosis and segmentation tasks. In this work, we propose a framework for selecting the proper bitrate for medical video compression, focusing on maintaining segmentation performance with minimal impact on accuracy. By leveraging the SALI segmentation model and a diverse set of video-based and frame-based features, the framework predicts the Dice score for videos compressed at different bitrates, enabling informed decision-making. To evaluate our work, SUN-SEG dataset is used. Two approaches were evaluated: one prioritizing high compression ratios while maintaining an acceptable Dice score threshold, and the other optimizing segmentation accuracy. In the first approach, the framework achieved an average Dice score of 77.66 with a compression ratio of 37.06 and in the second approach, the Dice score improved to 81.23 with a more conservative compression ratio of 8.16. These results highlight the adaptability of the framework to varying application requirements, such as telemedicine and medical video storage. This framework ensures efficient medical video compression while preserving segmentation accuracy, optimizing bandwidth for telemedicine, storage, remote diagnostics, and surgical video analysis, ultimately enhancing AI-assisted clinical decision-making.