CLIC: Workshop and Challenge on Learned Image Compression 2022
in conjunction with CVPR 2022

Website: http://www.compression.cc/

Introduction

Our workshop aims to gather publications which will advance the field of image and video compression using machine learning and computer vision. We want to encourage the development of novel encoder/decoder architectures, novel ways to control information flow between the encoder and the decoder, new perceptual losses, and new ways to learn quantized representations.

At the workshop we will also present the winners of our annual compression challenge. Like the workshop, the challenge is designed to encourage the development of new learned codecs. But it is also an opportunity to evaluate and compare end-to-end trained approaches against classical approaches and every submission is welcome.

Challenges

There are three challenge tracks.
(1) In the image compression track, images need to be compressed to 0.075 bpp, 0.15 bpp, and 0.3 bpp (bits per pixel).
(2) In the video compression track, short video clips need to be compressed to around 1 mbits and 0.1 mbits.
(3) Finally, in the perceptual metric track, human preferences on pairs of images will have to be predicted. The image pairs will come from the decoders submitted to the image compression track.


Regular Paper Track

We will have a short (4 pages) regular paper track, which allows participants to share research ideas related to image compression. In addition to the paper, we will host a poster session during which authors will be able to discuss their work in more detail.

Our workshop aims to gather publications which will advance the field of image and video compression using machine learning and computer vision. We invite you to submit papers on topics including but not limited to:

● novel encoder/decoder architectures
● novel ways to control information flow between the encoder and the decoder
● new perceptual losses
● new ways to learn quantized representations
● artefact removal, denoising, or super-resolution
● generative models with applications to compression

In addition, we ask our challenge participants to submit a paper describing their submission.

And in particular, how these topics can improve image compression.


Challenge Paper Track

The challenge task participants are asked to submit a short paper (up to 4 pages) detailing the algorithms which they submitted as part of the challenge.


Submission

A paper submission has to be in English, in pdf format, and at most 4 pages (excluding references) in CVPR style. The paper format must follow the same guidelines as for all CVPR submissions.
https://cvpr2022.thecvf.com/author-guidelines
The review process is double blind. Authors do not know the names of the chair/reviewers of their papers. Reviewers do not know the names of the authors.
Dual submission is allowed with CVPR main conference only. If a paper is submitted also to CVPR and accepted, the paper cannot be published both at the CVPR and the workshop.

For the paper submissions, please go to the online submission site
https://cmt3.research.microsoft.com/CLIC2022

Accepted and presented papers will be published after the conference in the CVPR Workshops Proceedings on by IEEE (http://www.ieee.org) and Computer Vision Foundation (www.cv-foundation.org).

The author kit provides a LaTeX2e template for paper submissions. Please refer to the example for detailed formatting instructions. If you use a different document processing system then see the CVPR author instruction page.

Author Kit: https://cvpr2022.thecvf.com/sites/default/files/2021-10/cvpr2022-author_kit-v1_1-1.zip

Important Dates

Jan 20, 2022 Challenge dataset is released
Mar 20, 2022 Challenge validation phase ends
Mar 23, 2022 Challenge test set is released
Mar 27, 2022 Competition closes
Mar 23, 2022 Paper submission deadline
Apr 01, 2022 Paper decision notification
Apr 08, 2022 Perceptual track test phase ends
Apr 08, 2022 Camera ready deadline



Speakers:

Tsachy Weissman, Stanford University
Debargha Mukherjee, Google
Zhou Wang, University of Waterloo
Auke Wiggers, Qualcomm
Guo Lu, Beijing Institute of Technology

Organizers:

George Toderici (Google)
Radu Timofte (Uni Wurzburg and ETH Zurich)
Lucas Theis (Google)
Johannes Ballé (Google)
Eirikur Agustsson (Google)
Nick Johnston (Google)
Fabian Mentzer (Google)
Zeina Sinno (Apple)
Andrey Norkin (Netflix)
Krishna Rapaka (Apple)
Erfan Noury (Apple)
Ross Cutler (Microsoft)
Luca Versari (Google)
Fabian Racape (Interdigital)


Sponsors (TBU):

Google
ETH Zurich / CVL
Apple
Netflix
Interdigital


Webpage:

http://www.compression.cc/