With the large amount of media content being downloaded and streamed across the internet, minimizing bandwidth while maintaining quality remains a constant challenge. In 2015, researchers demonstrated that neural network-based image compression could yield significant improvements to image resolution while retaining good quality and high compression speed. Continued advances in compression and bandwidth optimization techniques were stimulated in part by two successful workshops that we hosted at CVPR in 2018 and 2019.
Today, we are excited to announce the Third Workshop and Challenge On Learned Image Compression (CLIC) at CVPR 2020. This workshop challenges researchers to use machine learning, neural networks and other computer vision approaches to increase the quality and lower the bandwidth needed for multimedia transmission. This year’s workshop will also include two challenges: a low-rate image compression challenge and a P-Frame video compression challenge.
Similar to previous years, the goal of the low-rate image compression challenge is to compress an image dataset to 0.15 bits per pixel while maintaining the highest possible quality. Finalists will be selected by measuring their performance against the PSNR and MS-SSIM evaluation metrics. The final ranking will then be determined by a human evaluated rating task.
This year we are also introducing a P-Frame compression track, the first video compression task in this series. In this challenge, participants must first generate a transformation between two adjacent video frames. In the decompression part of the task, participants then use the first frame and their compressed representation to reconstruct the second frame. This challenge will be ranked based solely on the MS-SSIM performance score.
If you are doing research in the field of learned image compression or video compression, we encourage you to participate in CLIC, whether in the two competitions or the paper-only track for publications to be presented at the workshop at CVPR 2020. The validation server is currently available for submissions. The deadline for the final submission of the test set is March 23rd, 2020. For more details on the competition and an up-to-date schedule, please refer to compression.cc. Additional announcements and answers to questions can be found on our Google Groups page.
Acknowledgements
This workshop is being jointly hosted by researchers at Google, Twitter and ETH Zurich. We’d like to thank: George Toderici (Google), Nick Johnston (Google), Johannes Ballé (Google), Eirikur Agustsson (Google), Lucas Theis (Google), Wenzhe Shi (Twitter), Radu Timofte (ETH Zurich) and Fabian Mentzer (ETH Zurich) for their contributions.
by: Google AI via https://www.AiUpNow.com/
