让图像压缩按语义层级渐进传输，一码多用更高效。
Coarse-to-Fine: Progressive Image Compression for Semantically Hierarchical Classification
- 基于语义层次分解特征通道，实现粗到细的渐进编码
- 低码率下粗粒度识别性能显著提升，高码率保持细粒度准确
- 适合需要分层任务适配的视觉系统，解释性强
近年来，学习型图像压缩（LIC）推动了实际应用，促进了面向机器的图像压缩与渐进编码方案的研究。然而，二者融合仍不充分：现有渐进式机器编码主要针对样本级难易自适应（即从易到难），未考虑语义级可扩展性。本文提出一种语义层次感知的渐进编码器，支持从单一码流实现语义可扩展性（即粗到细）。我们首先基于CLIP嵌入对ImageNet-1K类别进行系统性语义层次划分。基于通道级自回归框架，将潜在表示分解为层级有序的通道块，每个块专门优化对应语义层次。大量实验表明，该方法在低码率下显著提升粗粒度识别性能，同时在高码率下维持细粒度准确性。通过语义可扩展视角重构渐进传输，本工作为任务自适应图像编码提供了高效且可解释的解决方案，在层次化评估中优于现有渐进编码器。
原文摘要 · Abstract (English)
Recent advances in learned image compression (LIC) have enabled practical deployments, spurring active research into image compression for machines and progressive coding schemes. However, their integration remains under-explored: prior works on progressive machine codec predominantly target sample-level difficulty adaptation (i.e., easy-to-hard), without considering semantic-level scalability. In this work, we introduce a semantic hierarchy-aware progressive codec that enables semantic scalability (i.e., coarse-to-fine) from a single bitstream. We first systematically categorize ImageNet-1K classes into CLIP embedding-based semantic hierarchies. Based on a channel-wise autoregressive framework, we decompose latent representations into hierarchically ordered channel blocks, each explicitly optimized for a corresponding semantic hierarchy. Extensive experiments demonstrate that our approach substantially improves coarse-level recognition at low bitrates while maintaining fine-grained accuracy at higher bitrates. By reframing progressive transmission through the lens of semantic scalability, our work provides an efficient and interpretable solution for task-adaptive image coding, outperforming existing progressive codecs under hierarchical evaluation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。