ADVANCING THE RATE-DISTORTION-COMPUTATION FRONTIER FOR NEURAL IMAGE COMPRESSION

The rate-distortion performance of neural image compression models has exceeded the state-of-the-art of 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 because of the lack of standard benchmarking platforms and by 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% overVTM and 3.0% over ELIC) without requiring significantly more FLOPs than other learning-based codecs

• Machine Perception

• Algorithms and Theory