#image compression

Discover a novel method for image representation and compression using 2D Gaussian Splatting that boasts high-speed rendering up to 2000 FPS and reduced GPU memory use. This approach surpasses JPEG in performance, integrating vector quantization for efficient neural image codecs, and is ideal for resource-constrained devices.

TensorFlow Compression provides cutting-edge tools to compress data for machine learning models effectively, ensuring minimal storage use and maintaining performance. These tools integrate seamlessly with TensorFlow and include features like range coding and entropy models. Although new feature development has halted, it supports TF 2.14. Comprehensive installation guides and API documentation facilitate smooth project maintenance. Available on PyPI, users can explore tutorials and discussions for optimal usage.

Stable Cascade leverages advanced compression techniques based on the Würstchen architecture to improve inference speed and lower training costs by significantly reducing latent space size. This model is compatible with various extensions like finetuning and ControlNet, ensuring high accuracy in prompt alignment and quality aesthetics. It demonstrates notable efficiency gains over models like Stable Diffusion, making it suitable for applications that prioritize speed and cost efficiency. The model provides flexible options for image compression and generation, accompanied by comprehensive resources for training and inference.

CompressAI is an open-source PyTorch library designed for end-to-end research in image and video compression. It includes specialized layers and operations for deep learning-based data compression and a partial integration of TensorFlow compression tools. It provides pre-trained models for image compression and scripts to evaluate model performance compared to traditional codecs. The library is compatible with Python 3.8+ and PyTorch 1.7+, with experimental multi-GPU support. Installation can be done via pip or from source, with Docker support in development. Extensive documentation supports usage and model training, making it a valuable resource for researchers.