Review of Compression Techniques in Text, Image and Video Compression in Medical Imaging Applications

Abstract

Efficient storage, transmission, and management of large datasets are crucial in medical imaging. This review examines various compression techniques utilized for text, image, and video data in medical imaging applications. For textual data, it discusses methods such as Huffman coding, Lempel-Ziv-Welch (LZW), and Bzip2, emphasizing their effectiveness in compressing metadata and diagnostic reports. Image compression techniques are divided into lossless methods, including JPEG-LS, PNG, and lossless JPEG 2000, which maintain data integrity, and lossy methods like standard JPEG, lossy JPEG 2000, and HEVC, which strike a balance between quality and compression ratios. Video compression is explored through codecs like H.264/AVC and H.265/HEVC, focusing on their importance for real-time video streaming and storage in procedures and telemedicine. The review also highlights the influence of the DICOM standard on ensuring interoperability and examines emerging deep learning-based approaches that offer significant improvements in compression efficiency. It concludes by discussing the trade-offs between compression efficiency, image quality, and computational complexity, and the evolving landscape of medical image compression technologies