Golomb Coding in Data Compression with MATLAB Implementation

In this text, we will provide an in-depth exploration of Golomb coding in data compression, along with detailed implementation using MATLAB code and original test data. Golomb coding represents a lossless data compression technique that efficiently reduces data size while preserving all original information without any loss. The core algorithm operates by dividing input data into two main components: quotient and remainder. By strategically adjusting the coding parameter (m-value), users can optimize the balance between quotient and remainder sizes to achieve superior compression ratios. Our MATLAB implementation demonstrates key functions including golomb_encode() for compression and golomb_decode() for reconstruction, featuring efficient bit-level manipulation for optimal performance. We provide complete, well-documented MATLAB source code that enables readers to easily implement Golomb coding with proper error handling and edge case management. Additionally, we include original test datasets covering various data patterns (uniform, geometric, and real-world distributions) to facilitate practical testing and performance evaluation. The implementation showcases parameter optimization techniques and includes benchmarking utilities to compare compression efficiency across different data types. This resource aims to deliver valuable technical insights and assist readers in developing a comprehensive understanding of Golomb coding principles and their practical applications in modern data compression systems.