End to End Image Compression

End-to-end image compression uses deep learning to optimize the entire compression pipeline, aiming to achieve high compression ratios while minimizing information loss and preserving image quality for both human viewing and machine analysis. Current research focuses on integrating tasks like denoising and improving rate-distortion performance through novel architectures such as transformers and self-organizing neural networks, often incorporating frequency-based transforms for better interpretability and scalability. These advancements promise to improve efficiency in various applications, from cloud-based image processing and IoT devices to immersive video technologies, by enabling faster and more efficient image transmission and storage.