Lossless image compression using super-spatial prediction of structural components

We recognize that the key challenge in image compression is to efficiently represent and encode high-frequency image structural components, such as edges, patterns, and textures. Existing image compression schemes attempt to predict image data using its spatial neighborhood. In this work, we develop an efficient image compression scheme based on super-spatial prediction of structural units. This so-called super-spatial prediction breaks this neighborhood constraint, attempting to find an optimal prediction of structural components within the whole image domain. We consider only lossless image compression. Our extensive experimental results demonstrate that the proposed scheme is very competitive and even outperforms the state-of-the-art image compression methods.