Entropy Modeling

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11 papers4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Bound-Conditioned Latent Inference for Progressive Image Compression
Oct 22, 2026Jaeseok Jang, Seungmin Jeon, Kwang Pyo Choi +1Entropy Modeling Learned Image Compression
Korea University · Samsung Electronics
Information Theory of Random Composition Trees
Oct 17, 2026Doron Sivan, Weishun Zhong, Tankut Can +2Entropy Modeling
Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel · School of Natural Sciences, Institute for Advanced Study, Princeton, NJ, 08540, USA · Department of Physics, Emory University, Atlanta, GA 30322, USA
WaveLLM: Wavelet-Domain LLM Entropy Modeling for Lossless Image Compression
Oct 15, 2026Yue Yu, Bowei Kang, Changsheng Gao +2Entropy Modeling Lossless Data Compression
University of Science and Technology of China, Hefei, China · MOE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei, China · Nanyang Technological University, Singapore, Singapore +2
Rate-Distortion Adaptive Primitive Selection for Omnidirectional Gaussian Splatting
Sep 28, 2026Yulong Cheng, Youneng Bao, Junfeng Zhou +23DGS Compression 3D Gaussian Pruning
Harbin Institute of Technology, Shenzhen, China · Shenzhen University, Shenzhen, China
Visual Token Codec: Unleashing Spatial Redundancy for ViT Feature Coding
Aug 9, 2026Donghui Feng, Fengxi Zhang, Changsheng Gao +6Entropy Modeling Visual Token Compression
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China · College of Computing and Data Science, Nanyang Technological University, Singapore · Pengcheng Laboratory, Shenzhen, Guangdong, China +1
Proteus: A Truncation-Robust Entropy Model for Progressive LiDAR Compression
Aug 1, 2026Yihan Qiu, Xiaodong Lin, Baoquan Zhao +2Point Cloud Compression Entropy Modeling
Peking University · Sun Yat-sen University
Enhanced Neural Video Representation Compression across Extreme Complexity and Quality Scales
Jun 26, 2026Ho Man Kwan, Tianhao Peng, Fan Zhang +3Implicit Neural Representations Neural Video Compression
Visual Information Lab, University of Bristol, UK · Network Connectivity Services Research, BT, UK
Fine-Tuning Improves Information Conveyance in Language Models
May 29, 2026Yuwei Cheng, Weiyi Tian, Haifeng XuLanguage Model Generation Evaluation LLM Fine-Tuning
Department of Data Science University of Chicago Chicago, IL 60637 · Department of Computer Science University of Chicago Chicago, IL 60637
ChWDTA: Channel-wise Wavelet-Domain Transformer Attention and Entropy Modeling for Learned Image Compression
May 27, 2026Haisheng Fu, Runyu Yang, Feng Ding +5Learned Image Compression Hybrid CNN-Transformer Architectures
Electrical and Computer Engineering Department, The University of British Columbia, Vancouver, BC, Canada · School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada · School of Electronic Science and Technology, Eastern Institute of Technology, Ningbo, China +1
Entropy of Ukrainian
Apr 30, 2026Anton Lavreniuk, Mykyta Mudryi, Markiian ChakloshEntropy Modeling
Polish-Japanese Academy of Information Technology · University of the National Education Commission in Kraków
What and Where to Adapt: Structure-Semantics Co-Tuning for Machine Vision Compression via Synergistic Adapters
Apr 11, 2026Shaobo Liu, Haobo Xiong, Kai Liu +1Entropy Modeling Learned Image Compression
Xidian University

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Bound-Conditioned Latent Inference for Progressive Image Compression

Jaeseok Jang, Seungmin Jeon, Kwang Pyo Choi, Chang-Su Kim

Korea University · Samsung Electronics

Entropy Modeling Learned Image Compression

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Progressive image compression reconstructs images from prefixes of a single bitstream and therefore involves sequential latent inference under partial observations. Existing trit-plane codecs mainly treat decoded trits as discrete symbols for probability prediction, without fully exploiting their role as interval constraints on the underlying continuous latents. We propose bound-conditioned latent inference (BLI), a framework that reformulates progressive trit-plane coding as sequential latent inference under shrinking interval constraints. At each coding step, the trits decoded so far define lower and upper bounds for each latent element. BLI uses the center and width of these bounds, together with hyperprior information shared by the encoder and decoder, to jointly refine the Gaussian parameters

(μ,σ)

for entropy modeling and the latent estimate

\hat{y}

for reconstruction. As additional trits are decoded, the feasible intervals shrink monotonically, yielding tighter constraints for the next inference step and enabling flexible in-plane refinement schedules. Experiments on Kodak, CLIC, and JPEG-AI show that BLI achieves state-of-the-art rate-distortion performance among progressive image codecs, reducing BD-rate by

17.78\%

over DPICT and by

7.96\%

over CTC on Kodak, while replacing CTC's plane-specific predictors with a shared model that uses about

5.3\times

fewer parameters and achieves lower latency than CTC.