Enhancing Signal Processing Efficiency: A Novel Lossless Integer Compression Method (CIRB)

Data compression is critical in modern technological systems, enhancing memory storage efficiency, reducing transmission loads, improving device performance, and advancing data processing methodologies. In signal processing, data compression is essential for managing large volumes of data, such as audio, video, and sensor readings, where maintaining quality and integrity is paramount. Over recent decades, advancements in lossless data compression have been substantial, yet they still confront issues like restricted compression ratios and high computational costs. This research addresses these challenges by introducing a novel lossless integer compression method, CIRB, extendable to various data types, including those prevalent in signal processing. Unlike conventional approaches that compress binary representations of integers, CIRB aims to achieve higher compression ratios with reduced processing times by directly compressing integer values. In this paper, we demonstrate the integer compression methods utilized in modern signal processing, explain the CIRB approach, and evaluate their data processing performance in terms of compression ratio and computational cost. With up to 70% extra space saving and notable execution time improvements, CIRB contributes to bridging existing research gaps and advancing state-of-the-art data processing methodologies, providing a foundation for future research in the signal processing domain.