Slimmable Video Codec

Zhaocheng Liu, Luis Herranz, Fei Yang, Saiping Zhang, Shuai Wan, Marta Mrak, Marc Górriz Blanch; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 1743-1747


Neural video compression has emerged as a novel paradigm combining trainable multilayer neural networks 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.

Related Material

[pdf] [arXiv]
@InProceedings{Liu_2022_CVPR, author = {Liu, Zhaocheng and Herranz, Luis and Yang, Fei and Zhang, Saiping and Wan, Shuai and Mrak, Marta and Blanch, Marc G\'orriz}, title = {Slimmable Video Codec}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {1743-1747} }