Learning Based Image Compression

Learning-based image compression leverages deep neural networks to achieve superior rate-distortion performance compared to traditional methods, aiming to minimize data size while preserving image quality. Current research focuses on improving model architectures, such as transformers and autoencoders with hyperpriors, and incorporating techniques like attention mechanisms, normalizing flows, and variable-rate coding to enhance efficiency and scalability. This field is significant because it enables more efficient storage and transmission of image data, impacting various applications from remote sensing to visual recognition and potentially leading to new compression standards.