  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
