Papers › CompressAI: a PyTorch library and evaluation platform for end-to-end compression research

CompressAI: a PyTorch library and evaluation platform for end-to-end compression research

5 Nov 2020arXiv:2011.03029archive 2025-07-28

Jean Bégaint, Fabien Racapé, Simon Feltman, Akshay Pushparaja

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.

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