compressors
Project description
Compressors
Compressors is a library of containing flexible implementations of state-of-the-art compression systems.
Compressors is early in development, but aims to include:
- Lossless compression systems, which can be used standalone or as part of a lossy compression system.
- Energy compacting transforms like the DCT, MCDCT, Wavelet transforms, and Wavelet packet transform.
- Differentiable soft quantizers for learned compression
- DNN autoencoder building blocks for standard modalities such as stereo audio, and RGB image/video
- DNN autoencoder building blocks for arbitrary signal shapes and dimensions, allowing users to easily create codecs for non-standard inputs (spatial audio arrays, hyperspectral images, 3d volumes, etc)
- End-to-end codec pipelines that combine the above techniques
- Conventional distortion metrics (e.g. PSNR, SSIM) for training and evaluation
- DNN-based distortion metrics (e.g. LPIPS, DISTS) for training and evaluation
- Tools for creating machine-oriented codecs, which can be optimized for downstream applications in addition to one or more distortion metrics
Package
- Python package:
pip install compressors
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