CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
This provides a tool for researchers in compression to develop and compare learned methods with traditional codecs, though it is incremental as it builds on existing models.
The paper introduces CompressAI, a PyTorch library and platform for researching end-to-end image and video compression, including reimplemented state-of-the-art models and evaluation tools, with objective results reported using PSNR and MS-SSIM metrics on the Kodak dataset.
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.