Few-Shot Domain Adaptation for Learned Image Compression
<|reference_start|>Few-Shot Domain Adaptation for Learned Image Compression: Learned image compression (LIC) has achieved state-of-the-art rate-distortion performance, deemed promising for next-generation image compression techniques. However, pre-trained LIC models usually suffer from significant performance degradation when applied to out-of-training-domain images, implying their poor generalization capabilities. To tackle this problem, we propose a few-shot domain adaptation method for LIC by integrating plug-and-play adapters into pre-trained models. Drawing inspiration from the analogy between latent channels and frequency components, we examine domain gaps in LIC and observe that out-of-training-domain images disrupt pre-trained channel-wise decomposition. Consequently, we introduce a method for channel-wise re-allocation using convolution-based adapters and low-rank adapters, which are lightweight and compatible to mainstream LIC schemes. Extensive experiments across multiple domains and multiple representative LIC schemes demonstrate that our method significantly enhances pre-trained models, achieving comparable performance to H.266/VVC intra coding with merely 25 target-domain samples. Additionally, our method matches the performance of full-model finetune while transmitting fewer than $2\%$ of the parameters.<|reference_end|>
@article{zhang2024few-shot,
    title={Few-Shot Domain Adaptation for Learned Image Compression},
    author={Tianyu Zhang, Haotian Zhang, Yuqi Li, Li Li, Dong Liu},
    journal={arXiv preprint arXiv:2409.11111},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11111},
    primaryClass={eess.IV cs.CV}
}