Bilateral Context Modeling for Residual Coding in Lossless 3D Medical Image Compression

Xiangrui LIU, Meng WANG, Shiqi WANG, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Residual coding has gained prevalence in lossless compression, where a lossy layer is initially employed and the reconstruction errors ( i.e ., residues) are then losslessly compressed. The underlying principle of the residual coding revolves around the exploration of priors based on context modeling. Herein, we propose a residual coding framework for 3D medical images, involving the off-the-shelf video codec as the lossy layer and a Bilateral Context Modeling based Network (BCM-Net) as the residual layer. The BCM-Net is proposed to achieve efficient lossless compression of residues through exploring intra-slice and inter-slice bilateral contexts. In particular, a symmetry-based intra-slice context extraction (SICE) module is proposed to mine bilateral intra-slice correlations rooted in the inherent anatomical symmetry of 3D medical images. Moreover, a bi-directional inter-slice context extraction (BICE) module is designed to explore bilateral inter-slice correlations from bi-directional references, thereby yielding representative inter-slice context. Experiments on popular 3D medical image datasets demonstrate that the proposed method can outperform existing state-of-the-art methods owing to efficient redundancy reduction. Our code will be available on GitHub for future research.
Original languageEnglish
Pages (from-to)2502-2513
Number of pages12
JournalIEEE Transactions on Image Processing
Volume33
Early online date25 Mar 2024
DOIs
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
© 1992-2012 IEEE.

Keywords

  • Lossless 3D medical image compression
  • bilateral context
  • learned image compression

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