arXiv Computer Vision
Learning Optimal Lattice Vector Quantizers for End-to-end Neural Image Compression
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Tree-Structured Vector Quantization For Efficient And Progressive Image Compression
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Trainable Vector Quantization for Large Language Models
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Multi-scale Image Representation Compression
The paper introduces MIRC, an overfitted image codec that quantizes all components—including latents, synthesis network, and entropy models—within a single rate‑distortion objective, following the neural video representation codec NVRC. It adds a multi‑scale representation with cross‑stage parameter sharing to capture cross‑scale redundancy, achieving a 10.5% BD‑rate saving over VVC on the CLIC2020 professional validation set. MIRC also offers configurable decoding complexity ranging from 1.2 to 2.9 kMAC per pixel, allowing deployment to match specific resource budgets.
arXiv Computer Vision
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Multi-scale Image Representation Compression
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arXiv AI
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SQS: Bayesian DNN Compression through Sparse Quantized Sub-distributions
arXiv:2510.08999v2 Announce Type: replace-cross Abstract: Compressing large-scale neural networks is essential for deploying models on resource-constrained devices. Most existing methods adopt weight...
arXiv Computer Vision
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ResARC: Residual-Aware AutoRegressive Coding for Ultra-Low Bitrate Image Compression
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