Few-Shot Domain Adaptation for Learned Image Compression

• URL: http://arxiv.org/abs/2409.11111v1
• Date: Tue, 17 Sep 2024 12:05:29 GMT
• Title: Few-Shot Domain Adaptation for Learned Image Compression
• Authors: Tianyu Zhang, Haotian Zhang, Yuqi Li, Li Li, Dong Liu,
• Abstract summary: Learned image compression (LIC) has achieved state-of-the-art rate-distortion performance. LIC models usually suffer from significant performance degradation when applied to out-of-training-domain images. We propose a few-shot domain adaptation method for LIC by integrating plug-and-play adapters into pre-trained models.
• Score: 24.37696296367332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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