No Cover Image

Conference Paper/Proceeding/Abstract 293 views 137 downloads

Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling

Omniah Nagoor, J. Whittle, Jingjing Deng, Benjamin Mora Orcid Logo, Mark Jones Orcid Logo

2020 IEEE International Conference on Image Processing (ICIP), Pages: 2815 - 2819

Swansea University Authors: Omniah Nagoor, Jingjing Deng, Benjamin Mora Orcid Logo, Mark Jones Orcid Logo

Abstract

Data compression forms a central role in handling the bottleneck of data storage, transmission and processing. Lossless compression requires reducing the file size whilst maintaining bit-perfect decompression, which is the main target in medical applications. This paper presents a novel lossless com...

Full description

Published in: 2020 IEEE International Conference on Image Processing (ICIP)
ISBN: 978-1-7281-6396-3 9781728163956
ISSN: 1522-4880 2381-8549
Published: IEEE 2020
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa55355
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: Data compression forms a central role in handling the bottleneck of data storage, transmission and processing. Lossless compression requires reducing the file size whilst maintaining bit-perfect decompression, which is the main target in medical applications. This paper presents a novel lossless compression method for 16-bit medical imaging volumes. The aim is to train a neural network (NN) as a 3D data predictor, which minimizes the differences with the original data values and to compress those residuals using arithmetic coding. We evaluate the compression performance of our proposed models to state-of-the-art lossless compression methods, which shows that our approach accomplishes a higher compression ratio in comparison to JPEG-LS, JPEG2000, JP3D, and HEVC and generalizes well.
College: Faculty of Science and Engineering
Start Page: 2815
End Page: 2819