Context-Based Adaptive Binary Arithmetic Coding for ‎Advanced Compression of Medical Images

  • Authors

    • Praneel Kumar Peruru Research Scholar, Department of CSE, JNTUACEA, JNTUA, Ananthapuramu, India
    • Kasa Madhavi Professor, Department of CSE, JNTUACEA, JNTUA Ananthapuramu, India
    https://doi.org/10.14419/dp7j7a32

    Received date: July 25, 2025

    Accepted date: August 12, 2025

    Published date: November 1, 2025

  • Context-Based Adaptive Binary Arithmetic Coding (CABAC); Medical Image Compression; PSNR; Smart Healthcare Systems; SSIM.; ‎Telemedicine.

  • Abstract

    The rapid development of telemedicine and intelligent healthcare technologies necessitates high efficiency and accuracy in medical image ‎compression methods for effective remote diagnostics and precise treatment. This paper introduces a new Context-Based Adaptive Binary ‎Arithmetic Coding (CABAC) framework designed to specifically compress sensitive healthcare images, such as MRI, CT, and X-ray ‎images. Like conventional techniques such as JPEG and JPEG2000, which can corrupt important diagnostic information through lossy ‎compression, the proposed CABAC-based algorithm leverages the statistical and distinctive nature of medical images to adaptively model ‎the context and optimize binary arithmetic coding of the images. Therefore, it leads to increased compression ratios while maintaining ‎diagnostically important essential image quality. The CABAC framework combines preprocessing, binarization, statistical context modeling, ‎and binary arithmetic coding to achieve more compression efficiency. Quantitative analyses of conventional datasets at The Cancer Imaging ‎Archive (TCIA) demonstrate that the proposed method achieves a compression ratio of up to 15:1, surpassing the capacity of JPEG and ‎JPEG2000. Moreover, the technique also guarantees large Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) ‎values, which reveal the high visual and structural quality of the decomposed medical images. Designed with computational efficiency in ‎mind, the model is well-suited for integration into real-time telemedicine technologies, such as innovative healthcare systems featuring AI-capable diagnostics and IoT-enabled medical devices. This method provides a viable remedy for bandwidth optimization, as well as ‎addressing storage needs and improving the accuracy of diagnostic tests, especially in technically limited environments‎.

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  • How to Cite

    Peruru, P. K. ., & Madhavi, K. . (2025). Context-Based Adaptive Binary Arithmetic Coding for ‎Advanced Compression of Medical Images. International Journal of Basic and Applied Sciences, 14(SI-1), 649-655. https://doi.org/10.14419/dp7j7a32