Insights from Generative Modeling for Neural Video Compression

• URL: http://arxiv.org/abs/2107.13136v2
• Date: Sun, 9 Jul 2023 23:05:59 GMT
• Title: Insights from Generative Modeling for Neural Video Compression
• Authors: Ruihan Yang, Yibo Yang, Joseph Marino, Stephan Mandt
• Abstract summary: We present newly proposed neural video coding algorithms through the lens of deep autoregressive and latent variable modeling. We propose several architectures that yield state-of-the-art video compression performance on high-resolution video. We provide further evidence that the generative modeling viewpoint can advance the neural video coding field.
• Score: 31.59496634465347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While recent machine learning research has revealed connections between deep generative models such as VAEs and rate-distortion losses used in learned compression, most of this work has focused on images. In a similar spirit, we view recently proposed neural video coding algorithms through the lens of deep autoregressive and latent variable modeling. We present these codecs as instances of a generalized stochastic temporal autoregressive transform, and propose new avenues for further improvements inspired by normalizing flows and structured priors. We propose several architectures that yield state-of-the-art video compression performance on high-resolution video and discuss their tradeoffs and ablations. In particular, we propose (i) improved temporal autoregressive transforms, (ii) improved entropy models with structured and temporal dependencies, and (iii) variable bitrate versions of our algorithms. Since our improvements are compatible with a large class of existing models, we provide further evidence that the generative modeling viewpoint can advance the neural video coding field.

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