[visionlist] CFP: Workshop and Challenge on Learned Image Compression (CLIC) @ CVPR 2018

Radu Timofte timofte.radu at gmail.com
Tue Feb 27 06:09:02 -05 2018

Apologies for cross-posting


CLIC: Workshop and Challenge on Learned Image Compression 2018
in conjunction with CVPR 2018, June 18, Salt Lake City, USA.

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


The domain of image compression has traditionally used approaches discussed
in forums such as ICASSP, ICIP and other very specialized venues like PCS,
DCC, and ITU/MPEG expert groups. This workshop and challenge will be the
first computer-vision event to explicitly focus on these fields. Many
techniques discussed at computer-vision meetings have relevance for lossy
compression. For example, super-resolution and artifact removal can be
viewed as special cases of the lossy compression problem where the encoder
is fixed and only the decoder is trained. But also inpainting,
colorization, optical flow, generative adversarial networks and other
probabilistic models have been used as part of lossy compression pipelines.
Lossy compression is therefore a potential topic that can benefit a lot
from a large portion of the CVPR community.

Recent advances in machine learning have led to an increased interest in
applying neural networks to the problem of compression. At CVPR 2017, for
example, one of the oral presentations was discussing compression using
recurrent convolutional networks. In order to foster more growth in this
area, this workshop will not only try to encourage more development but
also establish baselines, educate, and propose a common benchmark and
protocol for evaluation. This is crucial, because without a benchmark, a
common way to compare methods, it will be very difficult to measure

We propose hosting an image-compression challenge which specifically
targets methods which have been traditionally overlooked, with a focus on
neural networks (but also welcomes traditional approaches). Such methods
typically consist of an encoder subsystem, taking images and producing
representations which are more easily compressed than the pixel
representation (e.g., it could be a stack of convolutions, producing an
integer feature map), which is then followed by an arithmetic coder. The
arithmetic coder uses a probabilistic model of integer codes in order to
generate a compressed bit stream. The compressed bit stream makes up the
file to be stored or transmitted. In order to decompress this bit stream,
two additional steps are needed: first, an arithmetic decoder, which has a
shared probability model with the encoder. This reconstructs (losslessly)
the integers produced by the encoder. The last step consists of another
decoder producing a reconstruction of the original image.

In the computer vision community many authors will be familiar with a
multitude of configurations which can act as either the encoder and the
decoder, but probably few are familiar with the implementation of an
arithmetic coder/decoder. As part of our challenge, we therefore will
release a reference arithmetic coder/decoder in order to allow the
researchers to focus on the parts of the system for which they are experts.
While having a compression algorithm is an interesting feat by itself, it
does not mean much unless the results it produces compare well against
other similar algorithms and established baselines on realistic benchmarks.
In order to ensure realism, we have collected a set of images which
represent a much more realistic view of the types of images which are
widely available (unlike the well established benchmarks which rely on the
images from the Kodak PhotoCD, having a resolution of 768x512, or Tecnick,
which has images of around 1.44 megapixels). We will also provide the
performance results from current state-of-the-art compression systems as
baselines, like WebP and BPG.

Challenge Tasks

We provide two datasets: Dataset P (“professional”) and Dataset M
(“mobile”). The datasets are collected to be representative for images
commonly used in the wild, containing thousands of images.

The challenge will allow participants to train neural networks or other
methods on any amount of data (it should be possible to train on the data
we provide, but we expect participants to have access to additional data,
such as ImageNet).
Participants will need to submit a decoder executable that can run in the
provided docker environment and be capable of decompressing the submission
files. We will impose reasonable limitations for compute and memory of the
decoder executable.

We will rank participants (and baseline image compression methods – WebP,
JPEG 2000, and BPG) based on multiple criteria: (a) decoding speed; (b)
proxy perceptual metric (e.g., MS-SSIM Y); and (c) will utilize scores
provided by human raters. The overall winner will be decided by a panel,
whose goal is to determine the best compromise between runtime performance
and bitrate savings.

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
● Entropy minimization
● Image super-resolution for compression
● Deblurring
● 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 materials detailing the
algorithms which they submitted as part of the challenge. Furthermore, they
are invited to submit a paper detailing their approach for the challenge.

Important Dates

● December 24th, 2017 Challenge announcement and the training part of the
dataset released
● January 15th, 2018 The validation part of the dataset released, online
validation server is made available
● April 15th, 2018 The test set is released
● April 22th, 2018 The competition closes and participants are expected to
have submitted their Docker image along with the compressed versions of the
test set
● April 26th, 2018 Deadline for factsheets
● May 29th, 2018 Results are released to the participants
● June 04th, 2018 Deadline for paper submission


Please check out the discussion forum of the challenge for announcements
and discussions related to the challenge:


Ramin Zabih (Google)
Oren Rippel (WaveOne)
Jim Bankoski (Google)
Jens Ohm (RWTH Aachen)


William T. Freeman (MIT)
George Toderici (Google)
Michele Covell (Google)
Wenzhe Shi (Twitter)
Radu Timofte (ETH Zurich)
Lucas Theis (Twitter)
Johannes Ballé (Google)
Eirikur Agustsson (ETH Zurich)
Nick Johnston (Google)



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