Learned Image Compression with Hierarchical Progressive Context Modeling

TL;DR

This paper introduces a Hierarchical Progressive Context Model for learned image compression that efficiently captures long-range dependencies and diverse contextual information, leading to state-of-the-art compression performance.

Contribution

The paper proposes a novel hierarchical coding schedule and progressive context fusion mechanism for improved context modeling in image compression.

Findings

Achieves state-of-the-art rate-distortion performance

Balances compression quality and computational complexity

Demonstrates effective long-range dependency modeling

Abstract

Context modeling is essential in learned image compression for accurately estimating the distribution of latents. While recent advanced methods have expanded context modeling capacity, they still struggle to efficiently exploit long-range dependency and diverse context information across different coding steps. In this paper, we introduce a novel Hierarchical Progressive Context Model (HPCM) for more efficient context information acquisition. Specifically, HPCM employs a hierarchical coding schedule to sequentially model the contextual dependencies among latents at multiple scales, which enables more efficient long-range context modeling. Furthermore, we propose a progressive context fusion mechanism that incorporates contextual information from previous coding steps into the current step, effectively exploiting diverse contextual information. Experimental results demonstrate that our method achieves state-of-the-art rate-distortion performance and strikes a better balance between compression performance and computational complexity. The code is available at https://github.com/lyq133/LIC-HPCM.