  This paper presents CompressAI, a platform that provides custom operations,
layers, models and tools to research, develop and evaluate end-to-end image and
video compression codecs. In particular, CompressAI includes pre-trained models
and evaluation tools to compare learned methods with traditional codecs.
Multiple models from the state-of-the-art on learned end-to-end compression
have thus been reimplemented in PyTorch and trained from scratch. We also
report objective comparison results using PSNR and MS-SSIM metrics vs.
bit-rate, using the Kodak image dataset as test set. Although this framework
currently implements models for still-picture compression, it is intended to be
soon extended to the video compression domain.
