Poster

Hierarchical Autoregressive Modeling for Neural Video Compression

Ruihan Yang · Yibo Yang · Joe Marino · Stephan Mandt

Keywords: [ Autoregressive Models ] [ Generative Models ] [ Video Compression ] [ Compression ]

Abstract:

Recent work by Marino et al. (2020) showed improved performance in sequential density estimation by combining masked autoregressive flows with hierarchical latent variable models. We draw a connection between such autoregressive generative models and the task of lossy video compression. Specifically, we view recent neural video compression methods (Lu et al., 2019; Yang et al., 2020b; Agustssonet al., 2020) as instances of a generalized stochastic temporal autoregressive transform, and propose avenues for enhancement based on this insight. Comprehensive evaluations on large-scale video data show improved rate-distortion performance over both state-of-the-art neural and conventional video compression methods.

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