Neural image representations have recently emerged as a promising technique for storing, streaming, and rendering visual data. Coupled with learning-based workflows, these novel representations have demonstrated remarkable visual fidelity and memory efficiency. However, existing neural image representations often rely on explicit uniform data structures without content adaptivity or computation-intensive implicit models, limiting their adoption in real-time graphics applications. Inspired by recent advances in radiance field rendering, we propose Image-GS, a content-adaptive image representation. Using anisotropic 2D Gaussians as the basis, Image-GS shows high memory efficiency, supports fast random access, and offers a natural level of detail stack. Leveraging a tailored differentiable renderer, Image-GS fits a target image by adaptively allocating and progressively optimizing a set of 2D Gaussians. The generalizable efficiency and fidelity of Image-GS are validated against several recent neural image representations and industry-standard texture compressors on a diverse set of images. Notably, its memory and computation requirements solely depend on and linearly scale with the number of 2D Gaussians, providing flexible controls over the trade-off between visual fidelity and run-time efficiency. We hope this research offers insights for developing new applications that require adaptive quality and resource control, such as machine perception, asset streaming, and content generation.

翻译：神经图像表示技术近年来已成为存储、流式传输和渲染视觉数据的前沿方法。结合基于学习的工作流程，这些新型表示方法展现出卓越的视觉保真度与内存效率。然而，现有神经图像表示通常依赖显式均匀数据结构而缺乏内容自适应性，或采用计算密集的隐式模型，限制了其在实时图形应用中的推广。受辐射场渲染领域最新进展启发，我们提出Image-GS——一种内容自适应的图像表示方法。该方法以各向异性二维高斯函数为基础，具备高内存效率、支持快速随机访问，并能自然生成多层级细节堆栈。通过定制化的可微分渲染器，Image-GS能够通过自适应分配并渐进优化二维高斯函数集合来拟合目标图像。我们在多样化图像数据集上，将Image-GS与多种近期神经图像表示方法及工业标准纹理压缩器进行对比，验证了其泛化效率与保真度。值得注意的是，其内存与计算需求仅取决于二维高斯函数的数量并呈线性缩放，为视觉保真度与运行时效率的权衡提供了灵活控制。本研究有望为开发需要自适应质量与资源控制的新应用（如机器感知、资产流式传输与内容生成）提供技术启示。