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

Source: [Coarse-to-Fine: Progressive Image Compression for Semantically Hierarchical Classification](<https://www.sota2.com/research/paper/6a02b3f3dbc836b87d824560>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-05-13T05:00:05.563Z

Authors: Jungwoo Kim, Jun-Hyuk Kim, Jong-Seok Lee

Published: 2026-05-08T02:08:35.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2605.08266>)

[PDF](<https://arxiv.org/pdf/2605.08266.pdf>)