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Enhanced Framework for Lossless Image Compression Using Image Segmentation and a Novel Dynamic Bit-Level Encoding algorithm

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APPLIED SCIENCES-BASEL 2025年第6期15卷 2964-2964页

作者： Erdal, Erdal Onal, Alperen Kirikkale Univ Fac Engn & Nat Sci Dept Comp Engn TR-71450 Kirikkale Turkiye Kirikkale Univ Grad Sch Nat & Appl Sci Dept Comp Engn TR-71450 Kirikkale Turkiye

This study proposes a dynamic bit-level encoding algorithm (DEA) and introduces the S+DEA compression framework, which enhances compression efficiency by integrating the DEA with image segmentation as a preprocessing step. The novel approaches were validated on four different datasets, demonstrating strong performance and broad applicability. A dedicated data structure was developed to facilitate lossless storage and precise reconstruction of compressed data, ensuring data integrity throughout the process. The evaluation results showed that the DEA outperformed all benchmark encoding algorithms, achieving an improvement percentage (IP) value of 45.12, indicating its effectiveness as a highly efficient encoding method. Moreover, the S+DEA compression algorithm demonstrated significant improvements in compression efficiency. It consistently outperformed BPG, JPEG-LS, and JPEG2000 across three datasets. While it performed slightly worse than JPEG-LS in medical images, it remained competitive overall. A dataset-specific analysis revealed that in medical images, the S+DEA performed close to the DEA, suggesting that segmentation alone does not enhance compression in this domain. This emphasizes the importance of exploring alternative preprocessing techniques to enhance the DEA's performance in medical imaging applications. The experimental results demonstrate that the DEA and S+DEA offer competitive encoding and compression capabilities, making them promising alternatives to existing frameworks.

关键词： image compression encoding algorithm data structure image segmentation adaptive and self-organizing algorithm

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