Enhancing Signal Processing Efficiency: A Novel Lossless Integer Compression Method (CIRB)
Paper
Zhang, Ji, Farhat, Mohamad Khalil, Tao, Xiaohui and Li, Tianning. 2025. "Enhancing Signal Processing Efficiency: A Novel Lossless Integer Compression Method (CIRB)." 2024 IEEE 17th International Conference on Signal Processing (ICSP). Suzhou, China 28 - 31 Oct 2024 China. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/ICSP62129.2024.10846506
Paper/Presentation Title | Enhancing Signal Processing Efficiency: A Novel Lossless Integer Compression Method (CIRB) |
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Presentation Type | Paper |
Authors | Zhang, Ji, Farhat, Mohamad Khalil, Tao, Xiaohui and Li, Tianning |
Journal or Proceedings Title | Proceedings of 2024 IEEE 17th International Conference on Signal Processing (ICSP) |
Journal Citation | pp. 63-68 |
Number of Pages | 6 |
Year | 2025 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | China |
ISBN | 9798350387384 |
9798350387377 | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICSP62129.2024.10846506 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/10846506 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/10845915/proceeding |
Conference/Event | 2024 IEEE 17th International Conference on Signal Processing (ICSP) |
Event Details | 2024 IEEE 17th International Conference on Signal Processing (ICSP) Delivery In person Event Date 28 to end of 31 Oct 2024 Event Location Suzhou, China |
Abstract | 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. © 2024 IEEE. |
Keywords | CIRB method; Integer compression; Signal processing; Lossless Compression |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460199. Applied computing not elsewhere classified |
400999. Electronics, sensors and digital hardware not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Southern Queensland |
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