Workshop and Challenge on Learned Image Compression


Dec 18: The website of the 2019 edition of the workshop/challenge is online!
Jan 10: The evaluation server is online!
Jan 19: The leaderboard is up!
Feb 8: The prizes, of value more than 20000$, have been announced!


Our workshop aims to gather publications which will advance the field of image compression with and without neural networks. 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). 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, and learn how to quantize (or learn to quantize) better.

Important Dates

Date Description
December 17th 2018 Development phase & announcement. The training part of the dataset released.
January 8th, 2019 The validation part of the dataset released, online validation server is made available.
April 8th, 2019 Deadline for regular paper submission.
April 17th, 2019 The test set is released.
April 22th, 2019 Regular paper decision notification.
April 24th, 2019 The competition closes and participants are expected to have submitted their solutions along with the compressed versions of the test set.
May 8th, 2019 Deadline for challenge paper submission and factsheets.
May 15th, 2019 Results are released to the participants.
May 22rd, 2019 Challenge paper decision notification
May 30th, 2019 Camera ready deadline (all papers)


Anne Aaron


Anne Aaron is Director of Video Algorithms at Netflix and leads the team responsible for video analysis, processing and encoding in the Netflix cloud-based media pipeline. The team is tasked with generating the best quality video streams for millions of Netflix members worldwide. The team is also actively involved in defining next-generation video through academic research collaboration and standardization work. Prior to Netflix, Anne had technical lead roles at Cisco, working on the software deployed with millions of Flip Video cameras, Dyyno, an early stage startup which developed a real-time peer-to-peer video distribution system, and Modulus Video, a broadcast video encoder company. During her Ph.D. studies at Stanford University, she was a member of the Image, Video and Multimedia Systems Laboratory, led by Prof. Bernd Girod. Her research was one of the pioneering work in the sub-field of Distributed Video Coding. Anne is originally from Manila, Philippines. She holds B.S. degrees in Physics and Computer Engineering from Ateneo de Manila University and M.S. and Ph.D. degrees in Electrical Engineering from Stanford University. Anne was recognized by Forbes as one of America’s Top 50 Women In Tech in 2018. Anne recently binged on Ozark, Bodyguard and Bojack Horseman. Read More

Aaron van den Oord


Aaron van den Oord works as a research scientist at DeepMind, London. His research focuses on generative models and representation learning. Aaron completed his PhD at the University of Ghent in Belgium where he worked on generative models, image compression and music recommendation. After he joined DeepMind in 20 15 he made important contributions to the field of generative modeling with autoregressive networks, including PixelRNN, PixelCNN and WaveNet. He also developed new techniques for speeding up generative models for text-to-speech synthesis, which are now used in Google products such as the Google Assistant. In Aaron's most recent work he focused on representation learning with VQ-VAE and Contrastive Predictive Coding.Read More

Jyrki Alakuijala


Dr. Jyrki Alakuijala is an active member of the open source software community, and a data compression researcher. Jyrki works at Google as a Technical Lead/Manager, and his recent published work has been with Zopfli, Butteraugli, Guetzli, Gipfeli, WebP lossless, and Brotli compression formats and algorithms, and two hashing algorithms, CityHash and HighwayHash. Before his Google employment he developed software for neurosurgery and radiation therapy treatment planning.Read More

Workshop Schedule

Time Description
TBA Poster Setup
TBA Introduction of the dataset & challenges in creating and rating
TBA Invited Speaker
TBA Invited Speaker
TBA Invited Speaker
TBA Fastest Entry (among top-5 for 0.15bpp)
TBA 2nd Place Entry (0.15bpp)
TBA 1st Place Entry (0.15bpp)
TBA Break & Poster Session
TBA 2nd Place Entry (transparent)
TBA 1st Place Entry (transparent)
TBA Panel Discussion