Neural Image

Neural image compression aims to leverage deep learning to achieve superior rate-distortion performance compared to traditional codecs, focusing on efficient encoding and decoding of images and videos. Current research emphasizes developing novel architectures like variational autoencoders (VAEs) and implicit neural representations (INRs), often incorporating techniques such as hierarchical structures, quantization rectifiers, and adaptive attention mechanisms to improve compression efficiency and visual quality. These advancements are significant because they promise to reduce storage needs and bandwidth requirements for image and video data across various applications, from mobile devices to cloud-based services.