Fast In-Memory Data Compression Algorithm (inline C/C++) 480+MB/s compress, 2800+MB/s decompress, ratio% better than LZ4, Snappy, and Zstd@-1
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Updated
May 20, 2025 - C++
Fast In-Memory Data Compression Algorithm (inline C/C++) 480+MB/s compress, 2800+MB/s decompress, ratio% better than LZ4, Snappy, and Zstd@-1
🗜️ Library and application for lossless, format-preserving, two-pass optimization and repair of Vorbis data, reducing its size without altering any audio information.
TinyZZZ includes GZIP, LZ4, ZSTD, LZMA compression algorithms written in C language, unlike the official code, this code mainly focuses on simplicity and easy to understand.
An online .txt file compressor, de-compressor tool which uses Huffman Coding for Lossless data compression.
A tool to generate optimized hardware files for univariate functions.
A tool to generate optimized hardware files for univariate functions.
Really High Efficient File Optimizer will losslessly recompress files as much as possible according to their mime-types
Lossless audio encoding based in Golomb Rice Coding algorithm.
Simple tool to compress the entire set of photos and videos in a directory.
Data compression algorithms in C++
CLI tool for video processing (H.265/HEVC encoding & QuickTime compatibility) using FFmpeg, and batch lossless image compression with format preservation.
Nuxt3 | Shadcn | TailwindCSS | InspiraUI | File-comression app using ffmpeg scripts
Experimental Hrust compressor with 8bits buffer. Forked from https://gitlab.com/eugene77/optimal-hrust-compressor
Huffman Coding implementation in Java for lossless data compression and decompression using a priority queue-based Huffman tree.
Lossless data compression algorithm, aimed at fast decompression speeds and better compression ratios than LZ4HC and other speed-oriented algorithms
Fast & free image compression tool that actually works
Serialize + compress Pandas DataFrames.
FLAC lossless audio encoder-decoder for education purposes
MATLAB implementation of lossless image compression techniques for preserving image quality.
This project focuses on lossless compression techniques optimizing space, time, and energy for multiplications between binary (or ternary) matrix formats and real-valued vectors.
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