The inherent noise in the observed (e.g., scanned) binary document image
degrades the image quality and harms the compression ratio through breaking the
pattern repentance and adding entropy to the document images. In this paper, we
design a cost function in Bayesian framework with dictionary learning.
Minimizing our cost function produces a restored image which has better quality
than that of the observed noisy image, and a dictionary for representing and
encoding the image. After the restoration, we use this dictionary (from the
same cost function) to encode the restored image following the
symbol-dictionary framework by JBIG2 standard with the lossless mode.
Experimental results with a variety of document images demonstrate that our
method improves the image quality compared with the observed image, and
simultaneously improves the compression ratio. For the test images with
synthetic noise, our method reduces the number of flipped pixels by 48.2% and
improves the compression ratio by 36.36% as compared with the best encoding
methods. For the test images with real noise, our method visually improves the
image quality, and outperforms the cutting-edge method by 28.27% in terms of
the compression ratio.

使用贝叶斯框架和字典学习设计了一种代价函数，可以重建出更高质量的二进制文档图像并构建出表示和编码该图像的字典，实验结果表明该方法相对于原始噪音图像在视觉效果和压缩比上得到了显著提升。

基于模型的二值文档图像压缩迭代恢复与字典学习

Model-based Iterative Restoration for Binary Document Image Compression  with Dictionary Learning

This paper introduces the concept of adaptive temporal compressive sensing
(CS) for video. We propose a CS algorithm to adapt the compression ratio based
on the scene's temporal complexity, computed from the compressed data, without
compromising the quality of the reconstructed video. The temporal adaptivity is
manifested by manipulating the integration time of the camera, opening the
possibility to real-time implementation. The proposed algorithm is a
generalized temporal CS approach that can be incorporated with a diverse set of
existing hardware systems.

本文提出了一种基于时域自适应性压缩感知的视频压缩算法，可以根据场景的时域复杂度自适应地改变压缩比，且不会影响重建视频的质量，通过调整摄像机的积分时间来实现时域的适应性，可以与各种硬件系统集成。

视频的自适应时间压缩感知

Adaptive Temporal Compressive Sensing for Video

Today there are many universal compression algorithms, but in most cases is
for specific data better using specific algorithm - JPEG for images, MPEG for
movies, etc. For textual documents there are special methods based on PPM
algorithm or methods with non-character access, e.g. word-based compression. In
the past, several papers describing variants of word-based compression using
Huffman encoding or LZW method were published. The subject of this paper is the
description of a word-based compression variant based on the LZ77 algorithm.
The LZ77 algorithm and its modifications are described in this paper. Moreover,
various ways of sliding window implementation and various possibilities of
output encoding are described, as well. This paper also includes the
implementation of an experimental application, testing of its efficiency and
finding the best combination of all parts of the LZ77 coder. This is done to
achieve the best compression ratio. In conclusion there is comparison of this
implemented application with other word-based compression programs and with
other commonly used compression programs.

该论文描述了基于 LZ77 算法的一种基于词的压缩变体，包括滑动窗口实现和输出编码的各种方法。同时，还包括了一个实验性应用程序的实现，测试其效率并寻找最佳组合，以实现最佳压缩比的目标。最后，作者对该实现应用程序与其他基于词的压缩程序以及其他常用压缩程序进行了比较。