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

Praneel Kumar Peruru; Kasa Madhavi

doi:10.14419/dp7j7a32

Article Details

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

Authors:

Praneel Kumar Peruru | Kasa Madhavi

Publisher:

Science Publishing Corporation

Journal:

International Journal of Basic and Applied Sciences Vol.14 Issue SI-1, pp.649-655

Document Type:

Article

Publication Date:

1/2025

Document Language:

English

Document ID (Org No.):

2001387759

pISSN:

2227-5053

DOI:

10.14419/dp7j7a32

Keywords:

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.

Article Details

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

Authors:

Praneel Kumar Peruru | Kasa Madhavi

Publisher:

Science Publishing Corporation

Journal:

International Journal of Basic and Applied Sciences Vol.14 Issue SI-1, pp.649-655

Document Type:

Article

Publication Date:

1/2025

Document Language:

English

Document ID (Org No.):

2001387759

pISSN:

2227-5053

DOI:

10.14419/dp7j7a32

Context-Based Adaptive Binary Arithmetic Coding (CABAC)

Medical Image Compression

PSNR

Smart Healthcare Systems

SSIM.

\u200eTelemedicine.

Issues

Article Details

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

Authors: Praneel Kumar Peruru | Kasa Madhavi

Publisher: Science Publishing Corporation

Journal: International Journal of Basic and Applied Sciences Vol.14 Issue SI-1, pp.649-655

Document Type: Article

Publication Date: 1/2025

Document Language: English

Document ID (Org No.): 2001387759

pISSN: 2227-5053

DOI: 10.14419/dp7j7a32

Context-Based Adaptive Binary Arithmetic Coding (CABAC)

Medical Image Compression

PSNR

Smart Healthcare Systems

SSIM.

\u200eTelemedicine.

Issues

Authors:

Praneel Kumar Peruru | Kasa Madhavi

Publisher:

Science Publishing Corporation

Journal:

International Journal of Basic and Applied Sciences Vol.14 Issue SI-1, pp.649-655

Document Type:

Article

Publication Date:

1/2025

Document Language:

English

Document ID (Org No.):

2001387759

pISSN:

2227-5053

DOI:

10.14419/dp7j7a32