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。

利用多级字典进行无损图像压缩：二值图像

Lossless Image Compression Using Multi-level Dictionaries: Binary Images

Lossless image compression is an important technique for image storage and
transmission when information loss is not allowed. With the fast development of
deep learning techniques, deep neural networks have been used in this field to
achieve a higher compression rate. Methods based on pixel-wise autoregressive
statistical models have shown good performance. However, the sequential
processing way prevents these methods to be used in practice. Recently,
multi-scale autoregressive models have been proposed to address this
limitation. Multi-scale approaches can use parallel computing systems
efficiently and build practical systems. Nevertheless, these approaches
sacrifice compression performance in exchange for speed. In this paper, we
propose a multi-scale progressive statistical model that takes advantage of the
pixel-wise approach and the multi-scale approach. We developed a flexible
mechanism where the processing order of the pixels can be adjusted easily. Our
proposed method outperforms the state-of-the-art lossless image compression
methods on two large benchmark datasets by a significant margin without
degrading the inference speed dramatically.

本文提出一种多尺度渐进统计模型，兼顾像素级别处理和多尺度处理，能够在不显著降低推理速度的情况下，在两个大型基准数据集上显著优于现有的无损图像压缩方法。

多尺度渐进统计模型实现无损图像压缩

Lossless Image Compression Using a Multi-Scale Progressive Statistical  Model

We introduce a simple and efficient lossless image compression algorithm. We
store a low resolution version of an image as raw pixels, followed by several
iterations of lossless super-resolution. For lossless super-resolution, we
predict the probability of a high-resolution image, conditioned on the
low-resolution input, and use entropy coding to compress this super-resolution
operator. Super-Resolution based Compression (SReC) is able to achieve
state-of-the-art compression rates with practical runtimes on large datasets.
Code is available online at this https URL

介绍了一种简单高效的无损图像压缩算法，存储低分辨率版本，再经过多次无损超分辨率加工，采用熵编码来压缩超分辨率操作符，能够在大型数据集上以实用的运行时间实现最先进的压缩率。

通过超分辨率进行无损图像压缩

Lossless Image Compression through Super-Resolution

We leverage the powerful lossy image compression algorithm BPG to build a
lossless image compression system. Specifically, the original image is first
decomposed into the lossy reconstruction obtained after compressing it with BPG
and the corresponding residual. We then model the distribution of the residual
with a convolutional neural network-based probabilistic model that is
conditioned on the BPG reconstruction, and combine it with entropy coding to
losslessly encode the residual. Finally, the image is stored using the
concatenation of the bitstreams produced by BPG and the learned residual coder.
The resulting compression system achieves state-of-the-art performance in
learned lossless full-resolution image compression, outperforming previous
learned approaches as well as PNG, WebP, and JPEG2000.

利用 BPG 算法实现了一种损耗压缩图像的方法，该方法使用卷积神经网络模型对残差进行无损编码，通过与之前的学习方法以及 PNG、WebP、JPEG2000 进行比较，实现最先进的无损全分辨率图像压缩效果。