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Analysis of data streams to count value occurrences for use in lossless compression.
Distinct from Data Compression Algorithms: Focuses on the specific counting phase required for Huffman coding, distinct from the overall compression algorithm.
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This project is a comprehensive collection of common computer science algorithms and data structures implemented in Swift. It serves as an educational reference and library for studying computational complexity, algorithmic logic, and data structure engineering through practical code examples. The repository provides a wide suite of data structure implementations, including various types of linked lists, heaps, hash tables, and an extensive range of hierarchical trees such as Red-Black, B-Tree, and Splay trees. It also covers diverse sorting and searching techniques, from basic bubble sort to
Provides byte stream analysis to generate frequency tables for compression algorithms.
Mozjpeg is a high-performance C library for encoding, decoding, and transcoding JPEG images. It serves as a binary-compatible, drop-in replacement for standard JPEG libraries, maintaining existing function signatures to improve compression efficiency without requiring changes to application logic. The library functions as an image optimizer that reduces file sizes through lossless progressive encoding and coefficient optimization. It utilizes trellis-based quantization and SIMD-accelerated processing to optimize the trade-off between visual quality and file size. Its broader capabilities inc
Generates optimized encoding tables based on symbol frequency to maximize compression efficiency and minimize data stream size.