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Journal Article
DeepHQ: Learned Hierarchical Quantizer for Progressive Deep Image Coding
- Authors
- Jooyoung Lee, Se Yoon Jeong, Munchurl Kim
- Issue Date
- 2026-01
- Citation
- ACM Transactions on Multimedia Computing, Communications and Applications, v.22, no.1, pp.1-24
- ISSN
- 1551-6857
- Publisher
- Association for Computing Machinery
- Language
- English
- Type
- Journal Article
- Abstract
- Research on entropy model-based Learned Image Compression (LIC) has been actively progressing, leading to rapid advancements in coding efficiency. Beyond improvements in coding efficiency, LIC methods have also been explored for practical codec development. Despite these advancements, research on learned Progressive Image Coding (PIC) remains in its early stages. PIC aims to encode multiple quality levels into a single bitstream, improving bitstream versatility and achieving higher compression efficiency than simulcast compression. Existing learned PIC methods hierarchically quantize transformed latent representations with varying quantization step sizes. More specifically, these approaches progressively compress the additional information needed for quality improvement, considering that a wider quantization interval for lower-quality compression includes multiple narrower subintervals for higher-quality compression. However, they rely on handcrafted quantization hierarchies, leading to suboptimal compression efficiency. In this article, we propose a learned PIC method that first exploits learned quantization step sizes for each quantization layer. We also incorporate selective compression, ensuring that only essential representation components are retained in each quantization layer. Our experimental results demonstrate that the proposed method significantly enhances coding efficiency compared to the existing approaches while also reducing decoding time and model size. The source code is publicly available at https://github.com/JooyoungLeeETRI/DeepHQ.
- Keyword
- learned image compression, deep image compression, progressive coding
- KSP Keywords
- Coding efficiency, Decoding time, Early stages, Entropy model, Existing Approaches, Latent representations, PIC method, Progressive coding, Quality level, Quantization Interval, Source Code
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
