Feedback Recurrent Autoencoder for Video Compression
- URL: http://arxiv.org/abs/2004.04342v1
- Date: Thu, 9 Apr 2020 02:58:07 GMT
- Title: Feedback Recurrent Autoencoder for Video Compression
- Authors: Adam Golinski, Reza Pourreza, Yang Yang, Guillaume Sautiere, Taco S
Cohen
- Abstract summary: We propose a new network architecture for learned video compression operating in low latency mode.
Our method yields state of the art MS-SSIM/rate performance on the high-resolution UVG dataset.
- Score: 14.072596106425072
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advances in deep generative modeling have enabled efficient modeling
of high dimensional data distributions and opened up a new horizon for solving
data compression problems. Specifically, autoencoder based learned image or
video compression solutions are emerging as strong competitors to traditional
approaches. In this work, We propose a new network architecture, based on
common and well studied components, for learned video compression operating in
low latency mode. Our method yields state of the art MS-SSIM/rate performance
on the high-resolution UVG dataset, among both learned video compression
approaches and classical video compression methods (H.265 and H.264) in the
rate range of interest for streaming applications. Additionally, we provide an
analysis of existing approaches through the lens of their underlying
probabilistic graphical models. Finally, we point out issues with temporal
consistency and color shift observed in empirical evaluation, and suggest
directions forward to alleviate those.
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