We propose a new scheme to re-compress JPEG images in a lossless way. Using a
JPEG image as an input the algorithm partially decodes the signal to obtain
quantized DCT coefficients and then re-compress them in a more effective way.

我们提出了一种新的方案，以无损的方式重新压缩 JPEG 图像。使用 JPEG 图像作为输入，该算法部分解码信号以获得量化的 DCT 系数，然后以更有效的方式重新压缩它们。

基于实验的 JPEG 图像再压缩的分布预测

Distribution prediction for image compression: An experimental  re-compressor for JPEG images

Medical image compression is a widely studied field of data processing due to
its prevalence in modern digital databases. This domain requires a high color
depth of 12 bits per pixel component for accurate analysis by physicians,
primarily in the DICOM format. Standard raster-based compression of images via
filtering is well-known; however, it remains suboptimal in the medical domain
due to non-specialized implementations. This study proposes a lossless medical
image compression algorithm, CompaCT, that aims to target spatial features and
patterns of pixel concentration for dynamically enhanced data processing. The
algorithm employs fractal pixel traversal coupled with a novel approach of
segmentation and meshing between pixel blocks for preprocessing. Furthermore,
delta and entropy coding are applied to this concept for a complete compression
pipeline. The proposal demonstrates that the data compression achieved via
fractal segmentation preprocessing yields enhanced image compression results
while remaining lossless in its reconstruction accuracy. CompaCT is evaluated
in its compression ratios on 3954 high-color CT scans against the efficiency of
industry-standard compression techniques (i.e., JPEG2000, RLE, ZIP, PNG). Its
reconstruction performance is assessed with error metrics to verify lossless
image recovery after decompression. The results demonstrate that CompaCT can
compress and losslessly reconstruct medical images, being 37% more
space-efficient than industry-standard compression systems.

该研究提出了一种名为 CompaCT 的无损医学图像压缩算法，通过使用分形分割预处理，以及数据压缩的动态增强，实现了对医学图像的高效压缩和无损重建，其压缩比相比行业标准系统提高了 37%。

CompaCT：适用于无损压缩高色彩 DICOM 医学图像的基于分形的启发式像素分割

CompaCT: Fractal-Based Heuristic Pixel Segmentation for Lossless  Compression of High-Color DICOM Medical Images

Although deep neural networks are highly effective, their high computational
and memory costs severely challenge their applications on portable devices. As
a consequence, low-bit quantization, which converts a full-precision neural
network into a low-bitwidth integer version, has been an active and promising
research topic. Existing methods formulate the low-bit quantization of networks
as an approximation or optimization problem. Approximation-based methods
confront the gradient mismatch problem, while optimization-based methods are
only suitable for quantizing weights and could introduce high computational
cost in the training stage. In this paper, we propose a novel perspective of
interpreting and implementing neural network quantization by formulating
low-bit quantization as a differentiable non-linear function (termed
quantization function). The proposed quantization function can be learned in a
lossless and end-to-end manner and works for any weights and activations of
neural networks in a simple and uniform way. Extensive experiments on image
classification and object detection tasks show that our quantization networks
outperform the state-of-the-art methods. We believe that the proposed method
will shed new insights on the interpretation of neural network quantization.
Our code is available at
this https URL

该论文提出了一种将低位量化作为可微非线性函数的新方法，并通过该方法实现了神经网络的低位量化，并取得了比现有方法更好的效果。

量化网络

Quantization Networks

This paper addresses about various image compression techniques. On the basis
of analyzing the various image compression techniques this paper presents a
survey of existing research papers. In this paper we analyze different types of
existing method of image compression. Compression of an image is significantly
different then compression of binary raw data. To solve these use different
types of techniques for image compression. Now there is question may be arise
that how to image compress and which types of technique is used. For this
purpose there are basically two types are method are introduced namely lossless
and lossy image compression techniques. In present time some other techniques
are added with basic method. In some area neural network genetic algorithms are
used for image compression.
Keywords-Image Compression; Lossless; Lossy; Redundancy; Benefits of
Compression.

本研究调查了不同的图像压缩技术，针对不同的数据类型采用不同的技术，介绍了两种基本的图像压缩技术，以及在某些领域使用神经网络和遗传算法进行图像压缩的方法.