InterDigitalInc/CompressAI
A PyTorch library and evaluation platform for end-to-end compression research
What it solves
It provides a standardized PyTorch-based framework for researching and developing end-to-end data compression using deep learning, allowing researchers to move beyond classical codecs and create learned compression models for images and video.
How it works
CompressAI offers a suite of custom PyTorch operations, layers, and models specifically designed for learned compression. It includes a partial port of the TensorFlow compression library and provides pre-trained models that can be used immediately for encoding and decoding. The library also includes training scripts with rate-distortion loss and evaluation tools to compare the performance of learned models against traditional codecs like BPG, VTM, and x265.
Who it’s for
AI researchers and engineers specializing in data compression, computer vision, and deep learning who need a platform to train, evaluate, and benchmark learned image and video compression models.
Highlights
- End-to-End Research Platform: A complete pipeline from training to evaluation for deep learning-based compression.
- Pre-trained Model Zoo: Access to ready-to-use models for learned image compression.
- Benchmarking Tools: Built-in scripts to compare learned models against classical image and video compression standards.
- Flexible Training: Supports custom model implementation and distributed training across multiple GPUs.