Lossless image compression is required in various applications to reduce
storage or transmission costs of images, while requiring the reconstructed
images to have zero information loss compared to the original. Existing
lossless image compression methods either have simple design but poor
compression performance, or complex design, better performance, but with no
performance guarantees. In our endeavor to develop a lossless image compression
method with low complexity and guaranteed performance, we argue that
compressibility of a color image is essentially derived from the patterns in
its spatial structure, intensity variations, and color variations. Thus, we
divide the overall design of a lossless image compression scheme into three
parts that exploit corresponding redundancies. We further argue that the
binarized version of an image captures its fundamental spatial structure and in
this work, we propose a scheme for lossless compression of binary images.
The proposed scheme first learns dictionaries of $16\times16$, $8\times8$,
$4\times4$, and $2\times 2$ square pixel patterns from various datasets of
binary images. It then uses these dictionaries to encode binary images. These
dictionaries have various interesting properties that are further exploited to
construct an efficient scheme. Our preliminary results show that the proposed
scheme consistently outperforms existing conventional and learning based
lossless compression approaches, and provides, on average, as much as
$1.5\times$ better performance than a common general purpose lossless
compression scheme (WebP), more than $3\times$ better performance than a state
of the art learning based scheme, and better performance than a specialized
scheme for binary image compression (JBIG2).

基于空间结构和二值图像的模式，本研究提出了一种具有低复杂度和性能保证的无损图像压缩方案，通过学习字典从二值图像中编码，取得了比传统方法、学习方法和专用方案更好的压缩性能。