Neural Image Representation

Neural image representation aims to efficiently encode and decode images using neural networks, focusing on minimizing storage and computational costs while maintaining high visual fidelity. Current research emphasizes developing content-adaptive models, such as those employing 2D Gaussians or quantized Fourier features, to improve efficiency and learning speed compared to traditional methods. These advancements are impacting various applications, including video compression, image inpainting, and real-time graphics, by offering flexible control over the trade-off between visual quality and resource consumption. The development of more efficient and generalizable neural image representations is driving progress in fields requiring high-fidelity visual data processing with limited resources.