Search Results for "compression algorithm"

A Python-based framework for federated learning simulation, emphasizing modularity, communication efficiency, and algorithmic flexibility. Supports both server- and client-side customization for research and development purposes.

Crunch is an image compression tool for lossy PNG image file optimization. Using a combination of selective bit depth, color palette reduction and color type, as well as zopfli DEFLATE compression algorithm encoding that employs the pngquant and zopflipng PNG optimization tools, Crunch is effectively able to optimize and compress images with minimal decrease in image quality.

Crunch is a tool for lossy PNG image file optimization. It combines selective bit depth, color type, and color palette reduction with zopfli DEFLATE compression algorithm encoding using the pngquant and zopflipng PNG optimization tools. This approach leads to a significant file size gain relative to lossless approaches at the expense of a relatively modest decrease in image quality. Continuous benchmark testing is available in our GitHub Actions CI. Please see the benchmarks directory of this repository for details about the benchmarking approach and instructions on how to execute benchmarks locally on the reference images distributed in this repository or with your own image files.

LZHW Command Line Lossless Compression Tool is a Windows command line tool used to compress and decompress files from and to any form, csv, excel etc without any dependencies or installations. Using an optimized algorithm (LZHW) developed from Lempel-Ziv, Huffman and LZ-Welch algorithms. The tool can work in parallel and most of its code is written in Cython, so it is pretty fast.

Encryption with symmetric-key algorithm Kirmah - generate keys with exotic chars - redefine key length - key mark to ensure decryption capabilities - mix data - fast with multiprocessing - possible compression - gui on gtk3 - cli tool - python3 DEPENDENCIES : ======== python3, python-gobject, gobject-introspection, pygtk 3 modes are available to encrypt : ======== - compression (full / disabled or only final step) - random (simulate a random order - based on crypted key - to randomize data) - mix (mix data according to a generated map - based on crypted key - with addition of noise) Process is as follow : ======== encrypt : file > [ compress > ] encrypt > [randomiz data > mix data > compress > ] file.kmh decrypt: file.kmh > [ uncompress > unmix data > unrandomiz data] > decrypt > [uncompress > ] file ======== for encrypt/decrypt large binary files, use the fastest alternative : split/merge

...So I'm going to deobfuscate LZO and provide a ISC implementation of this algorithm in Python and C. In addition, I will provide a textual description of the algorithm so that it can be easily ported to any programming language. I expect a severe performance degradation, but I leave optimizing for speed to other people.