ResARC: Residual-Aware AutoRegressive Coding for Ultra-Low Bitrate Image Compression

arXiv:2609.39451 · cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.CV

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/kakaobrain/coyo-dataset

Terminology

• Quantization — 81× in this paper · explained in 12 episodes
• Fidelity — 25× in this paper · explained in 2 episodes
• Fine-Tuning — 25× in this paper · explained in 5 episodes
• Transformer — 16× in this paper · explained in 2 episodes
• Tokenization — 11× in this paper · explained in 2 episodes
• Adaptation — 10× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 8× in this paper
• Ablation — 7× in this paper · explained in 1 episode
• Diffusion Model — 7× in this paper · explained in 8 episodes
• Diffusion Models — 7× in this paper · explained in 6 episodes
• Latent Representation — 6× in this paper · explained in 2 episodes
• Flow Matching — 5× in this paper · explained in 7 episodes
• Decode — 4× in this paper · explained in 2 episodes
• International Conference on Computer Vision — 4× in this paper
• Ablation Studies — 2× in this paper · explained in 1 episode
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Visual Auto-Regressive — 2× in this paper
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptive Weighting — 1× in this paper · explained in 1 episode
• Distillation — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes

Sources

• Next-Frame Decoding for Ultra-Low-Bitrate Image Compression with Video Diffusion Priors
• PaddleOCR 3.0 Technical Report
• Asymmetric numeral systems: entropy coding combining speed of Huffman coding with compression rate of arithmetic coding
• High-Fidelity Image Compression with Score-based Generative Models
• CoD-Lite: Real-Time Diffusion-Based Generative Image Compression
• Differentiable Vector Quantization for Rate-Distortion Optimization of Generative Image Compression
• Ultra Lowrate Image Compression with Semantic Residual Coding and Compression-aware Diffusion
• ProGVC: Progressive-based Generative Video Compression via Auto-Regressive Context Modeling
• DINOv2: Learning Robust Visual Features without Supervision
• CADC: Content Adaptive Diffusion-Based Generative Image Compression
• DiT-IC: Aligned Diffusion Transformer for Efficient Image Compression
• Lossy Compression with Gaussian Diffusion

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