Super-High-Fidelity Image Compression via Hierarchical-ROI and Adaptive Quantization
<|reference_start|>Super-High-Fidelity Image Compression via Hierarchical-ROI and Adaptive Quantization: Learned Image Compression (LIC) has achieved dramatic progress regarding objective and subjective metrics. MSE-based models aim to improve objective metrics while generative models are leveraged to improve visual quality measured by subjective metrics. However, they all suffer from blurring or deformation at low bit rates, especially at below $0.2bpp$. Besides, deformation on human faces and text is unacceptable for visual quality assessment, and the problem becomes more prominent on small faces and text. To solve this problem, we combine the advantage of MSE-based models and generative models by utilizing region of interest (ROI). We propose Hierarchical-ROI (H-ROI), to split images into several foreground regions and one background region to improve the reconstruction of regions containing faces, text, and complex textures. Further, we propose adaptive quantization by non-linear mapping within the channel dimension to constrain the bit rate while maintaining the visual quality. Exhaustive experiments demonstrate that our methods achieve better visual quality on small faces and text with lower bit rates, e.g., $0.7X$ bits of HiFiC and $0.5X$ bits of BPG.<|reference_end|>
@article{luo2024super-high-fidelity,
    title={Super-High-Fidelity Image Compression via Hierarchical-ROI and Adaptive
  Quantization},
    author={Jixiang Luo, Yan Wang and Hongwei Qin},
    journal={arXiv preprint arXiv:2403.13030},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13030},
    primaryClass={eess.IV cs.CV}
}