  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.
