CVPR 2020- Workshop and Challenge on Learned Image Compression
CLIC 2020
- URL: https://www.compression.cc/
- Event Date: 2020-06-15 ~ 2020-06-15
- Submission Date: 2020-03-23
- Location: Seattle, United States of America
CLIC: Workshop and Challenge on Learned Image Compression 2020
in conjunction with CVPR 2020
Website: http://www.compression.cc/
Introduction
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.
Challenge Tracks
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.
Low-rate compression
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.
P-frame compression
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.
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
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 a short paper (up to 4 pages) detailing the algorithms which they submitted as part of the challenge.
Submission (TBA)
Important Dates
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 submitted.
March 16th, 2020 Test set is released for conestants 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 submission deadline.
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.
Speakers (TBA)
Nils Thuerey, Technical University of Munich, Germany
Yochai Blau, Technion, Israel
Tom Bird, UCL, London
Organizers:
George Toderici (Google)
Wenzhe Shi (Twitter)
Radu Timofte (ETH Zurich)
Lucas Theis (Twitter)
Johannes Ballé (Google)
Eirikur Agustsson (ETH Zurich)
Nick Johnston (Google)
Fabian Mentzer (ETH Zurich)
Sponsors (TBA)
Google
Twitter
ETH Zurich / CVL
Webpage:
http://www.compression.cc/
in conjunction with CVPR 2020
Website: http://www.compression.cc/
Introduction
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.
Challenge Tracks
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.
Low-rate compression
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.
P-frame compression
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.
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
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 a short paper (up to 4 pages) detailing the algorithms which they submitted as part of the challenge.
Submission (TBA)
Important Dates
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 submitted.
March 16th, 2020 Test set is released for conestants 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 submission deadline.
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.
Speakers (TBA)
Nils Thuerey, Technical University of Munich, Germany
Yochai Blau, Technion, Israel
Tom Bird, UCL, London
Organizers:
George Toderici (Google)
Wenzhe Shi (Twitter)
Radu Timofte (ETH Zurich)
Lucas Theis (Twitter)
Johannes Ballé (Google)
Eirikur Agustsson (ETH Zurich)
Nick Johnston (Google)
Fabian Mentzer (ETH Zurich)
Sponsors (TBA)
ETH Zurich / CVL
Webpage:
http://www.compression.cc/
Journal Promotion
|
| Acta Koreana (Acta Koreana) |
|
| Acta Via Serica (AVS) |
|
| Computers and Concrete (CAC) |
|
| Journal of Information Processing Systems (JIPS) |
|
| Journal of Internet Computing and Services (KSII - JICS) |
|
| Journal of Smart Tourism (Smart Tourism) |
|
| Membrane and Water Treatment (MWT) |
|
| Wind and Structures, An International Journal (Wind and Structures) |
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