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We propose sandwiched video compression -- a video compression system thatwraps neural networks around a standard video codec. The sandwich frameworkconsists of a neural pre- and post-processor with a standard video codecbetween them. The networks are trained jointly to optimize a rate-distortionloss function with the goal of significantly improving over the standard codecin various compression scenarios. End-to-end training in this setting requiresa differentiable proxy for the standard video codec, which incorporatestemporal processing with motion compensation, inter/intra mode decisions, andin-loop filtering. We propose differentiable approximations to key video codeccomponents and demonstrate that, in addition to providing meaningfulcompression improvements over the standard codec, the neural codes of thesandwich lead to significantly better rate-distortion performance in twoimportant scenarios.When transporting high-resolution video via low-resolutionHEVC, the sandwich system obtains 6.5 dB improvements over standard HEVC. Moreimportantly, using the well-known perceptual similarity metric, LPIPS, weobserve 30% improvements in rate at the same quality over HEVC. Last but notleast, we show that pre- and post-processors formed by verymodestly-parameterized, light-weight networks can closely approximate theseresults.

Sandwiched Video Compression: Efficiently Extending the Reach of Standard Codecs with Neural Wrappers