2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 13 , Issue 2 , PP: 192-201, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Saad H. Baiee 1 * , Tawfiq A. AL-Assadi 2
Doi: https://doi.org/10.54216/JISIoT.130215
Efficient compression algorithms are required to handle the growing amount of medical picture data, ensuring that storage and transmission requirements are met without compromising diagnostic quality. This research presents a hybrid image compression framework that integrates deep learning alongside standard lossless compression techniques. A convolutional autoencoder (CAE) learns a compact representation of medical images, which are subsequently compressed using the Brotli algorithm. Our technique beats conventional approaches, like JPEG, JPEG2000, and wavelet-based ones, according to an analysis of a brain MRI dataset. It maintains competitive compression ratios while producing higher (PSNR) and (MSE), indicating higher picture integrity and low information loss. To strike a good balance between the critical need for accurate diagnosis and the economical use of resources, this study offers a possible method for compressing medical images.
Deep Learning , Convolutional Auto encoders , Brotli Algorithm lossless compression , MSE , PSNR
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