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Practical Lossless Volumetric Medical Image Compression via Tri-plane Context Tree Learning — arXiv:2608.13897

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.13897
License (as archived): CC0-1.0
Archived: 2026-10-19T04:04:36.132Z
SHA-256 of the archived text: 01ef20cec43a1ca2a1f7b3b3d7ebe5d85dc84868b58fe6293fc6eba5defa3554
Stable URL of this page: https://metraipolis.com/documento/dc_eefffeac62d4

How to cite

Practical Lossless Volumetric Medical Image Compression via Tri-plane Context Tree Learning — arXiv:2608.13897 — arXiv (en), https://arxiv.org/abs/2608.13897 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_eefffeac62d4 (Metraipolis archive dc_eefffeac62d4, sha256 01ef20cec43a1ca2a1f7b3b3d7ebe5d85dc84868b58fe6293fc6eba5defa3554)

BibTeX

@misc{dceefffeac62d4,
  title = {Practical Lossless Volumetric Medical Image Compression via Tri-plane Context Tree Learning — arXiv:2608.13897},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.13897},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_eefffeac62d4; sha256 01ef20cec43a1ca2a1f7b3b3d7ebe5d85dc84868b58fe6293fc6eba5defa3554},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.13897. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Lossless compression of volumetric medical images is of paramount importance for clinical and research applications where data fidelity is essential. Traditional compression methods are often limited in efficiency due to rigid, handcrafted models. Conversely, deep neural network (DNN)-based compression methods, while effective, demand substantial computational resources, hindering deployment in resource-constrained settings. To address these challenges, we propose a novel tri-plane context tree (TCT)-based method for lossless volumetric medical image compression that delivers high performance without relying on DNNs or external training data. To exploit intra-slice and inter-slice redundancies, we introduce a compact tri-plane context representation that decomposes complex 3D context modeling into efficient 2D modeling on three orthogonal planes. By integrating this representation with a context tree framework, we develop an input-specific TCT model employing an adaptive binary tree structure. At each tree node, the model dynamically selects from a suite of tri-plane based predictors and contextual feature extractors, enabling data-adaptive context modeling tailored to local structural characteristics. Instead of offline training, we sample a subset of the input volume to learn the TCT model by optimizing the minimum description length (MDL) through iterative construction and pruning. With the learned TCT model, each pixel retrieves its corresponding context, computes the prediction residual using the predictor dictated by the context, and performs entropy encoding based on the associated histograms. Experimental results demonstrate that the proposed method achieves compression performance on par with recent DNN-based methods on multiple datasets, while maintaining low computational cost and fast coding speeds, making it highly applicable in practice.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-17 in cs.CV).
Categorie arXiv: Image and Video Processing (eess.IV) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.13897 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T03:57:58.044Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).