Compression is essential to storing and transmitting medical videos, but the effect of compression on downstream medical tasks is often ignored. Furthermore, systems in practice rely on standard video codecs, which naively allocate bits between medically relevant frames or parts of frames. In this work, we present an empirical study of some deficiencies of classical codecs on gastroenterology videos, and motivate our ongoing work to train a learned compression model for colonoscopy videos. We show that two of the most common classical codecs, H264 and HEVC, compress medically relevant frames statistically significantly worse than medically nonrelevant ones, and that polyp detector performance degrades rapidly as compression increases. We explain how a learned compressor could allocate bits to important regions and allow detection performance to degrade more gracefully. Many of our proposed techniques generalize to medical video domains beyond gastroenterology
翻译:压缩对下游医疗任务的影响往往被忽视。 此外,系统实际上依赖标准的视频编码器,这些编码器天真地在医学相关框架或框架部分之间分配比分。 在这项工作中,我们介绍了对胃肠录影带古典编码器的一些缺陷进行的经验研究,并激励我们目前为培养结肠镜录像学习的压缩模型而开展的工作。我们展示了两种最常用的古典编码器,即H264和HEVC,在统计上比医学上非相关框架要差得多得多的压缩机,而且聚合式探测器的性能随着压缩的增加而迅速降解。我们解释了一个学习的压缩机如何将比分分配给重要的区域,并允许探测性能更加优雅地降解。我们提出的许多技术都被概括到毒肠学以外的医疗视频领域。