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CLIC 2021 : CVPR 2021- Workshop and Challenge on Learned Image Compression


When Jun 19, 2021 - Jun 19, 2021
Where Virtual, Nashville, Tennessee, US
Submission Deadline Mar 23, 2021
Notification Due Apr 8, 2021
Final Version Due Apr 15, 2021
Categories    machine learning   compression   deep learning   generative models

Call For Papers

CLIC: Workshop and Challenge on Learned Image Compression 2021
in conjunction with CVPR 2021



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.


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 Mbit/s.
(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.

We encourage exploratory research which shows promising results in:

Lossy image compression
Quantization (learning to quantize; dealing with quantization in optimization)
Entropy minimization
Image super-resolution for compression
Compression artifact removal
Inpainting (and compression by inpainting)
Generative adversarial networks
Perceptual metrics optimization and their applications to compression

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.


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.
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

Accepted and presented papers will be published after the conference in the CVPR Workshops Proceedings on by IEEE ( and Computer Vision Foundation (

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:

Important Dates

Jan 10, 2021 Challenge dataset is released
Mar 20, 2021 Challenge validation phase ends
Mar 23, 2021 Paper submission deadline
Mar 23, 2021 Challenge test set is released
Mar 27, 2021 Competition closes
Apr 08, 2021 Paper decision notification


João Ascenso, University of Lisbon
Kede Ma, City University of Hong Kong
Rianne van den Berg, Google
Federico Perazzi, Facebook


George Toderici (Google)
Wenzhe Shi (Twitter)
Radu Timofte (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)


ETH Zurich / CVL
Facebook Reality Labs


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