惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

量子位
云风的 BLOG
云风的 BLOG
小众软件
小众软件
IT之家
IT之家
T
Tailwind CSS Blog
WordPress大学
WordPress大学
S
SegmentFault 最新的问题
美团技术团队
博客园 - 叶小钗
V
V2EX
博客园 - Franky
大猫的无限游戏
大猫的无限游戏
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
阮一峰的网络日志
阮一峰的网络日志
博客园 - 【当耐特】
罗磊的独立博客
博客园_首页
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
爱范儿
爱范儿
宝玉的分享
宝玉的分享
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Jina AI
Jina AI
月光博客
月光博客
有赞技术团队
有赞技术团队

math updates on arXiv.org

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization Non-normal spectral signatures of instability in neural network training dynamics Optimization of randomized neural networks for transfer operator approximation Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy Training-Free Looped Transformers Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries Asymmetric Scaling Laws from Sparse Features Is Dimensionality a Barrier for Retrieval Models? RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs Commutator-Induced Uncertainty in VAEs Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them Sparse In-Network Learning via Shortest-Path Backpropagation and Finite-Rate Gating Instance-Optimal Estimation with Multiple LLM Judges on a Budget Entropy Equivalence Testing Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation Any-Dimensional Invariant Universality Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models Anytime Training with Schedule-Free Spectral Optimization Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology The General Theory of Localization Methods Group-Algebraic Tensors: Provably-optimal Equivariant Learning and Physical Symmetry Discovery General Lower Bounds for Differentially Private Federated Learning with Arbitrary Public-Transcript Interactions PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels Proximal basin hopping: global optimization with guarantees
An Empirical Study of Entropy-Conserving Binarization in ...
[Submitted on 22 Jun 2026] · 2026-06-24 · via math updates on arXiv.org

View PDF HTML (experimental)

Abstract:CABAC, the entropy coder of H.264/AVC and the basis for HEVC and VVC, decomposes multi-symbol values into bins via a binarization scheme before a binary arithmetic coder. H.264 uses Truncated Unary plus k-th order Exp-Golomb (UEG); alternatives include canonical Huffman and the entropy-conserving binarization (ECB), which provably preserves entropy mapping m-ary data to m-1 binary strings but has not been evaluated inside a production binary arithmetic coder. We integrate ECB into a from-scratch CABAC implementation alongside UEG, single-context Huffman, and a Huffman variant with per-bin-position contexts (HuffmanPos), all sharing one M-coder backend. We benchmark all four on synthetic sources, DCT residuals from a procedural image, and the full 24-image Kodak suite (2,480 round-trip trials, bit-exact verified). On the procedural image, a sparsity-driven crossover at Q=8 lets ECB overtake single-context Huffman, reaching 27 percentage points below at Q=32. On Kodak the crossover shifts below the tested range and ECB beats single-context Huffman at every Q, the gap growing from 0.031 to 0.113 bits per symbol. HuffmanPos, sharing Huffman's codewords but allocating one context per bin position, beats ECB on 12 of 15 source cells and loses by at most 0.56 percentage points on the other three, despite the same per-symbol bin count as single-context Huffman. This isolates the dominant mechanism: at low source entropy the rate gap is driven primarily by context allocation over the bin stream, not the binarization's per-symbol bin count. ECB's rate efficiency costs 7 to 10x in decoder latency on large alphabets, traced to an O(N*m) decoder; we sketch an interleaved single-pass variant that would close this gap. Code, benchmarks, and raw data are open source.

Submission history

From: Vinamra Singh [view email]
[v1] Mon, 22 Jun 2026 01:28:46 UTC (148 KB)