Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaption
<|reference_start|>Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaption: Although recent generative image compression methods have demonstrated impressive potential in optimizing the rate-distortion-perception trade-off, they still face the critical challenge of flexible rate adaption to diverse compression necessities and scenarios. To overcome this challenge, this paper proposes a Controllable Generative Image Compression framework, Control-GIC, the first capable of fine-grained bitrate adaption across a broad spectrum while ensuring high-fidelity and generality compression. We base Control-GIC on a VQGAN framework representing an image as a sequence of variable-length codes (i.e. VQ-indices), which can be losslessly compressed and exhibits a direct positive correlation with the bitrates. Therefore, drawing inspiration from the classical coding principle, we naturally correlate the information density of local image patches with their granular representations, to achieve dynamic adjustment of the code quantity following different granularity decisions. This implies we can flexibly determine a proper allocation of granularity for the patches to acquire desirable compression rates. We further develop a probabilistic conditional decoder that can trace back to historic encoded multi-granularity representations according to transmitted codes, and then reconstruct hierarchical granular features in the formalization of conditional probability, enabling more informative aggregation to improve reconstruction realism. Our experiments show that Control-GIC allows highly flexible and controllable bitrate adaption and even once compression on an entire dataset to fulfill constrained bitrate conditions. Experimental results demonstrate its superior performance over recent state-of-the-art methods.<|reference_end|>
@article{li2024once-for-all:,
    title={Once-for-All: Controllable Generative Image Compression with Dynamic
  Granularity Adaption},
    author={Anqi Li, Yuxi Liu, Huihui Bai, Feng Li, Runmin Cong, Meng Wang, Yao
  Zhao},
    journal={arXiv preprint arXiv:2406.00758},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00758},
    primaryClass={eess.IV cs.CV cs.MM}
}