Brotli 1.1.0 🞉
Brotli is a generic-purpose lossless compression algorithm that compresses data using a combination of a modern variant of the LZ77 algorithm, Huffman coding and 2nd order context modeling, with a compression ratio comparable to the best currently available general-purpose compression methods.
cd Ports/brotli
./package.sh
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Brotli is an open-source data compression algorithm developed by Google. It is designed to provide high compression ratios while maintaining fast decompression speeds, making it well-suited for use in various applications such as web browsers, web servers, and content delivery networks (CDNs). Brotli is particularly optimized for compressing text-based data, such as HTML, CSS, and JavaScript files.
At its core, Brotli employs a combination of the LZ77 algorithm and a context modeling stage using a static dictionary and a series of context models. Here is a technical description of the key components and processes involved in Brotli:
1. LZ77 Compression: Brotli utilizes the LZ77 algorithm, which replaces repetitive sequences in the input data with backreferences. These backreferences point to previously seen matching sequences, allowing for more efficient storage and transmission of data. Brotli divides the input data into a series of 16-bit windows, and within each window, it searches for the longest backward match for a given position, encoding it as a backreference.
2. Static Dictionary: Brotli incorporates a pre-defined static dictionary consisting of commonly used words and phrases. The static dictionary is used to match and replace frequent sequences in the input data, further enhancing compression efficiency. By leveraging this dictionary, Brotli can achieve higher compression ratios for text-based data.
3. Context Modeling: Brotli employs a context modeling stage to analyze and predict the probability distribution of symbols based on their context. It uses a combination of context models, such as context tree models (CTMs) and context models with adaptive probability estimation (CMA). These models capture the statistical patterns and dependencies in the input data, allowing for more accurate prediction of the next symbol.
4. Huffman Coding: After applying LZ77 compression and context modeling, Brotli performs Huffman coding to encode the compressed data into variable-length codes. Huffman coding assigns shorter codes to frequently occurring symbols and longer codes to less frequent symbols, resulting in efficient representation of the compressed data.
5. Metadata and Block Structure: Brotli adds metadata to the compressed data, including information about the compression parameters, block sizes, and context model settings. The compressed data is divided into blocks, each containing a set of symbols, backreferences, and associated metadata. This block structure enables independent decoding of individual blocks, providing benefits for streaming scenarios and random access to compressed data.
6. Fast Decompression: Brotli emphasizes fast decompression speeds by utilizing a parallel decoding architecture. It employs a combination of Huffman decoding, inverse context modeling, and LZ77 decompression to efficiently reconstruct the original data from the compressed representation. Brotli's fast decompression makes it well-suited for scenarios where low latency and quick data access are crucial, such as web browsing.
Overall, Brotli combines LZ77 compression, a static dictionary, context modeling, Huffman coding, and a block-based structure to achieve high compression ratios for text-based data while maintaining fast decompression speeds. Its technical design enables efficient storage, transmission, and processing of compressed data, making it a popular choice for web-related applications.
Website: https://github.com/google/brotli
Port: https://github.com/SerenityOS/serenity/tree/master/Ports/brotli
Dependents: libjxl
