Image Rescaling

Image rescaling aims to efficiently compress and decompress images by creating high-quality low-resolution representations that can be accurately reconstructed to their original high-resolution counterparts. Recent research focuses on developing invertible neural networks, often incorporating residual blocks and flow-based models, to learn bijective mappings between high and low-resolution images, thereby minimizing information loss during downscaling and improving reconstruction fidelity. These advancements are improving image processing efficiency, particularly for ultra-high-definition media, and enabling real-time rescaling of very large images while optimizing for both quality and file size.