Optimizing PSNR and Compression Ratios for Efficient Medical Image Storage and Transmission Using a Hybrid Lossy-Lossless Framework

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 6

Year of Publication : 2025

Authors : Bhawesh Joshi, Gurveen Vaseer

10.14445/23488549/IJECE-V12I6P101

How to Cite?

Bhawesh Joshi, Gurveen Vaseer, "Optimizing PSNR and Compression Ratios for Efficient Medical Image Storage and Transmission Using a Hybrid Lossy-Lossless Framework," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 6, pp. 1-14, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I6P101

Abstract:

As medical imaging data keeps mounting exponentially, there is a growing need for powerful compression methods that can shrink storage requirements and lighten data transfer burdens without sacrificing diagnostic image quality. Through a combination of Convolutional Neural Networks and Support Vector Machines, this new hybrid lossy-lossless compression mechanism delivers a higher Peak Signal-to-Noise Ratio (PSNR) while achieving superior compression efficiency. The new framework merges sophisticated lossy approaches, including Discrete Wavelet Transform (DWT) and quantization methods, with a dependable lossless compression stage through entropy coding techniques. The combined use of CNNs for preprocessing with SVM-based adaptive region classification lets the system selectively encode and compress the image data so important diagnostic regions maintain the highest quality through an increased compression rate applied to less important areas.

Keywords:

Hybrid image compression, Medical image storage, Peak Signal-to-Noise Ratio (PSNR), Convolutional Neural Networks (CNNs), Lossy-lossless compression.

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