@InProceedings{ZhaochengLiu2022,
author="Zhaocheng Liu
and Luis Herranz
and Fei Yang
and Saiping Zhang
and Shuai Wan
and Marta Mrak
and Marc Gorriz",
title="Slimmable Video Codec",
booktitle="CVPR 2022 Workshop and Challenge on Learned Image Compression (CLIC 2022, 5th Edition)",
year="2022",
pages="1742--1746",
abstract="Neural video compression has emerged as a novel paradigm combining trainable multilayer neural net-works and machine learning, achieving competitive rate-distortion (RD) performances, but still remaining impractical due to heavy neural architectures, with large memory and computational demands. In addition, models are usually optimized for a single RD tradeoff. Recent slimmable image codecs can dynamically adjust their model capacity to gracefully reduce the memory and computation requirements, without harming RD performance. In this paper we propose a slimmable video codec (SlimVC), by integrating a slimmable temporal entropy model in a slimmable autoencoder. Despite a significantly more complex architecture, we show that slimming remains a powerful mechanism to control rate, memory footprint, computational cost and latency, all being important requirements for practical video compression.",
optnote="MACO; 601.379; 601.161",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3687), last updated on Thu, 20 Apr 2023 10:59:30 +0200",
doi="10.1109/CVPRW56347.2022.00183",
opturl="https://ieeexplore.ieee.org/document/9857273",
file=":http://refbase.cvc.uab.es/files/LHY2022.pdf:PDF"
}