Coarse-to-Fine: Progressive Image Compression for Semantically Hierarchical Classification

• URL: http://arxiv.org/abs/2605.08266v1
• Date: Fri, 08 May 2026 02:08:35 GMT
• Title: Coarse-to-Fine: Progressive Image Compression for Semantically Hierarchical Classification
• Abstract summary: We introduce a semantic hierarchy-aware progressive codecs that enables semantic scalability (i.e., coarse-to-fine) from a single bitstream.<n>Our work provides an efficient and interpretable solution for task-adaptive image coding, outperforming existing progressive hierarchical evaluation.
• Score: 27.208988763458958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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