Neural Image Compression

Neural image compression uses deep learning 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 efficiency and controllability, exploring architectures like variational autoencoders and transformers, often incorporating techniques such as predictive coding, adaptive quantization, and generative models to enhance both objective and perceptual fidelity. These advancements are significant for applications requiring efficient image storage and transmission, particularly in resource-constrained environments and for large-scale multimedia data management.