[visionlist] CFP: CVPR 2020 Workshop and Challenge on Learned Image Compression (CLIC)
timofte.radu at gmail.com
Wed Feb 19 13:20:34 -04 2020
Apologies for cross-posting
CALL FOR PARTICIPANTS & PAPERS
CLIC: 3rd Workshop and Challenge on Learned Image Compression 2020
in conjunction with CVPR 2020, June 14, Seattle, USA.
Our workshop aims to gather publications which will advance the field of
image and video compression using state of the art machine learning and
computer vision techniques. Even with the long history of signal-processing
oriented compression, taking new approaches to image processing have great
potential, due to the proliferation of high-resolution cell-phone images
and special hardware (e.g., GPUs and mobile AI accelerators). The potential
in this area has already been demonstrated using recurrent neural networks,
convolutional neural networks, and adversarial learning, many of these
matching the best image-compression standards when measured on perceptual
metrics. As such, we are interested in the various techniques associated
with this class of methods. Broadly speaking, we would like to encourage
the development of novel encoder/decoder architectures, novel ways to
control information flow between the encoder and the decoder, novel
optimization objectives for improved perceptual quality and learn how to
quantize (or learn to quantize) better.
There are two challenge tracks. In the low bit-rate track, images need to
be compressed to below 0.15 bits per pixel (bpp). This is the same task as
in previous years, which allows us to measure progress over the years. As a
first step towards video compression, this year also includes a P-frame
track. Here, video P-frames need to be predicted from a previous frame.
For the low bit-rate track (which is similar to the one we ran at CLIC
2018), contestants will be asked to compress the entire dataset to 0.15 bpp
or smaller. The winners of the competition will be chosen based human
perceptual rating task and will be asked to give a short talk at the CLIC
workshop. PSNR and MS-SSIM will be evaluated but not considered for prizes.
We will provide last year’s professional and mobile datasets (all splits)
as the training data for this challenge track. A new test set will be
generated for this year and released during the test phase.
The P-frame challenge will require entrants to compress a video frame
conditioned on the previous image frame. Instead of splitting the dataset
into training and test sets, in this track the entire dataset is released
before the test phase. To discourage overfitting, the model size is added
to the compressed dataset size and the sum cannot exceed a target bit-rate.
That is, participants should try to minimize both the dataset size and the
model size. The winner will be determined based on MS-SSIM.
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
● 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.
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
November 22th 2019 Development phase & announcement. The training part of
the dataset released.
January 7th, 2020 The validation part of the dataset released, online
validation server is made available.
March 13th, 2020 Final decoders for the challenge are expected to be
March 16th, 2020 Test set is released for contestants to compress.
March 20th, 2020 Encoded test set submission deadline. The competition is
closed at this point.
March 23th, 2020 Paper and Factsheet submission deadline.per and Factsheet
April 6th, 2020 Paper decision notification.
Mid April, 2020 Camera ready deadline for CVPR
Mid May, 2020 End of human evaluation on both challenges. Results will be
released online before the workshop.
Nils Thuerey, Technical University of Munich, Germany
Yochai Blau, Technion, Israel
Tom Bird, UCL, London
George Toderici (Google)
Wenzhe Shi (Twitter)
Radu Timofte (ETH Zurich)
Lucas Theis (Twitter)
Johannes Ballé (Google)
Eirikur Agustsson (ETH Zurich / Google)
Nick Johnston (Google)
Fabian Mentzer (ETH Zurich)
CVL / ETH Zurich
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