Introduction to CompressAI
CompressAI is a comprehensive PyTorch library dedicated to advancing research in end-to-end data compression. By offering a robust platform for deep learning-based data compression, CompressAI supports researchers and developers in exploring and developing more efficient compression algorithms.
Key Features
CompressAI offers a variety of tools and resources to aid in research and development:
- Custom Operations and Models: It includes bespoke operations, layers, and models that are specifically tailored for data compression tasks using deep learning.
- Integration with TensorFlow: Part of TensorFlow's official compression library is ported into CompressAI, allowing users familiar with TensorFlow to leverage their existing knowledge.
- Pre-trained Models: The library comes with pre-trained models for image compression, making it easier to start experimenting and evaluating different approaches without needing to train models from scratch.
- Evaluation Scripts: Scripts are provided to help compare novel models against traditional image and video compression codecs, enabling straightforward benchmarking of new research.
Installation Guide
Installing CompressAI is straightforward, supporting Python 3.8+ and PyTorch 1.7+. It can be installed via pip or from the source. For those preferring a custom setup, additional installation options are provided, including development, tutorial, and all-inclusive package installs. While official Docker images are anticipated, Conda environments are not officially supported as of now.
Documentation
Extensive documentation is available for CompressAI, covering installation processes, API details, model training tutorials, and a model zoo that lists available models. This documentation serves as a valuable resource for both new users and seasoned researchers.
Practical Usage
Example scripts and Jupyter notebooks demonstrate key functionalities:
- Image Encoding/Decoding: Using pre-trained models, users can encode and decode images via provided script examples.
- Model Training: There are outlined scripts for training models with rate-distortion loss, allowing researchers to apply their proprietary models using CompressAI.
- Model Evaluation: CompressAI facilitates the evaluation of trained models on custom datasets, providing scripts for assessing model performance and plotting results.
- Video Compression Tests: It includes capabilities for testing video compressions similar to those for images, although currently limited to specific models like ssf2020.
Testing and Contributions
CompressAI has a robust testing framework using pytest, allowing developers to validate their implementations. Contributions to the project are highly encouraged, with guidelines available for those wishing to report issues, suggest enhancements, or contribute code.
Licensing and Authors
CompressAI is distributed under the BSD 3-Clause Clear License. It is developed by a team of dedicated experts from the InterDigital AI Lab, including Jean Bégaint, Fabien Racapé, Simon Feltman, and Hyomin Choi.
Citing CompressAI
Researchers using CompressAI in their work are encouraged to reference the library in their publications, with citation formats provided for both the general project and specific works related to variable bitrate models.
Related Resources
CompressAI is linked with other notable projects and resources in the field of data compression, such as the TensorFlow compression library, HEVC HM, and VVC VTM reference software, among others. These connections help users explore a broader context and foster a deeper understanding of data compression technologies.
CompressAI serves as a critical tool for the research community, pushing the boundaries of how data is compressed efficiently in the age of vast information exchange.