New lossless compression method for medical images bypasses deep learning

Researchers have developed a new method for lossless compression of volumetric medical images that does not require deep neural networks or external training data. This approach, called the tri-plane context tree (TCT) method, utilizes a compact tri-plane context representation to model 3D context efficiently by analyzing three orthogonal planes. The TCT model is learned adaptively from the input volume itself, optimizing for minimum description length. Experiments show that this method achieves compression performance comparable to deep learning-based techniques while offering significantly lower computational costs and faster coding speeds, making it practical for real-world applications. AI

IMPACT Offers a practical alternative to deep learning for medical image compression, potentially reducing computational requirements.

RANK_REASON This is a research paper detailing a new method for image compression. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.CV →

• arXiv
• cs.CV
• deep neural network
• Hugging Face
• minimum description length
• Tri-plane Context Tree

• paper
• infra

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