End-to-End RGB-IR Joint Image Compression With Channel-wise Cross-modality Entropy Model
Journal:
arXiv
Published Date:
Jun 27, 2025
Abstract
RGB-IR(RGB-Infrared) image pairs are frequently applied simultaneously in
various applications like intelligent surveillance. However, as the number of
modalities increases, the required data storage and transmission costs also
double. Therefore, efficient RGB-IR data compression is essential. This work
proposes a joint compression framework for RGB-IR image pair. Specifically, to
fully utilize cross-modality prior information for accurate context probability
modeling within and between modalities, we propose a Channel-wise
Cross-modality Entropy Model (CCEM). Among CCEM, a Low-frequency Context
Extraction Block (LCEB) and a Low-frequency Context Fusion Block (LCFB) are
designed for extracting and aggregating the global low-frequency information
from both modalities, which assist the model in predicting entropy parameters
more accurately. Experimental results demonstrate that our approach outperforms
existing RGB-IR image pair and single-modality compression methods on LLVIP and
KAIST datasets. For instance, the proposed framework achieves a 23.1% bit rate
saving on LLVIP dataset compared to the state-of-the-art RGB-IR image codec
presented at CVPR 2022.
