Inpainting-based compression represents images in terms of a sparse subset of its pixel data. Storing the carefully optimised positions of known data creates a lossless compression problem on sparse and often scattered binary images. This central issue is crucial for the performance of such codecs. Since it has only received little attention in the literature, we conduct the first systematic investigation of this problem so far. To this end, we first review and compare a wide range of existing methods from image compression and general purpose coding in terms of their coding efficiency and runtime. Afterwards, an ablation study enables us to identify and isolate the most useful components of existing methods. With context mixing, we combine those ingredients into new codecs that offer either better compression ratios or a more favourable trade-off between speed and performance.

基于修复的压缩根据其像素数据的稀疏子集来表示图像。存储已知数据的精心优化的位置会在稀疏且通常分散的二进制图像上产生无损压缩问题。这个核心问题对于此类编解码器的性能至关重要。由于它在文献中很少受到关注，我们迄今为止对这个问题进行了第一次系统的调查。为此，我们首先回顾和比较图像压缩和通用编码的各种现有方法的编码效率和运行时间。之后，消融研究使我们能够识别和隔离现有方法中最有用的组件。通过上下文混合，