Dec-Adapter: Exploring Efficient Decoder-Side Adapter for Bridging Screen Content and Natural Image Compression

Abstract

Natural image compression has been greatly improved in the deep learning era. However, the compression performance will be heavily degraded if the pretrained encoder is directly applied on screen content image compression. Meanwhile, we observe that parameter-efficient transfer learning (PETL) methods have shown great adaptation ability in high-level vision tasks. Therefore, we propose a Dec-Adapter, a pioneering entropy-efficient transfer learning module for the decoder to bridge natural image and screen content compression. The adapter’s parameters are learned during encoding and transmitted to the decoder for image-adaptive decoding. Our Dec-Adapter is lightweight, domain-transferable, and architecture-agnostic with generalized performance in bridging the two domains. Experiments demonstrate that our method outperforms all existing methods by a large margin in terms of BD-rate performance on screen content image compression. Specifically, our method achieves over 2 dB gain compared with the baseline when transferred to screen content image compression.