  The rate-distortion performance of neural image compression models has
exceeded the state-of-the-art for non-learned codecs, but neural codecs are
still far from widespread deployment and adoption. The largest obstacle is
having efficient models that are feasible on a wide variety of consumer
hardware. Comparative research and evaluation is difficult due to the lack of
standard benchmarking platforms and due to variations in hardware architectures
and test environments. Through our rate-distortion-computation (RDC) study we
demonstrate that neither floating-point operations (FLOPs) nor runtime are
sufficient on their own to accurately rank neural compression methods. We also
explore the RDC frontier, which leads to a family of model architectures with
the best empirical trade-off between computational requirements and RD
performance. Finally, we identify a novel neural compression architecture that
yields state-of-the-art RD performance with rate savings of 23.1% over BPG
(7.0% over VTM and 3.0% over ELIC) without requiring significantly more FLOPs
than other learning-based codecs.
