We present Neural Space-filling Curves (SFCs), a data-driven approach to infer a context-based scan order for a set of images. Linear ordering of pixels forms the basis for many applications such as video scrambling, compression, and auto-regressive models that are used in generative modeling for images. Existing algorithms resort to a fixed scanning algorithm such as Raster scan or Hilbert scan. Instead, our work learns a spatially coherent linear ordering of pixels from the dataset of images using a graph-based neural network. The resulting Neural SFC is optimized for an objective suitable for the downstream task when the image is traversed along with the scan line order. We show the advantage of using Neural SFCs in downstream applications such as image compression. Code and additional results will be made available at https://hywang66.github.io/publication/neuralsfc.

翻译：我们介绍神经空间填充曲线(SFCs),这是一种由数据驱动的方法,用以推断一套图像的基于背景的扫描顺序。像素线性排序是许多应用的基础,如图像基因模型中使用的视频拼拼拼、压缩和自动递减模型。现有的算法采用固定扫描算法,如Raster扫描或Hilbert扫描。相反,我们的工作利用一个基于图形的神经网络从图像数据集中学习了空间一致的像素线性排序。由此产生的Neural SFC被优化,以达到在图像与扫描线顺序一起穿行时适合下游任务的目标。我们展示了在图像压缩等下游应用中使用神经SFCs的优势。代码和其他结果将在https://hywang66.github.io/publication/neuralsfc上公布。