Biresidual Compression Network With Conditional Diffusion Model for Hyperspectral Image Compression
Jiahui Liu, Lili Zhang, Jingang Wang, Lele Qu · IEEE Transactions on Geoscience and Remote Sensing · 2025
Hyperspectral image (HSI) compression presents the challenge of preserving both spectral and spatial fidelity while achieving high compression rates. Current compression methods frequently depend on band-by-band compression or simplistic joint modeling, which complicates the balance between spectral consistency and perceptual quality. To address this issue, a compression driven generation framework (BRC-CDM) is proposed, which decouples the extraction of compressed representations from the reconstruction of high-quality images. We introduce reference band information through channel-level concatenation to guide the spectral residual compression network in collaboratively extracting residual information in both spatial and spectral domains. During the prediction phase, a spectral gaussian grid compensation structure is further integrated to enhance the accuracy of predictions for the target bands. Ultimately, by compressing the residual information based on the differences between the predicted bands and the true bands, an efficient representation of the residual information in the compressed domain is achieved. The predicted image is then fused with the compressed residual to obtain a more expressive and potentially compressed representation. During the decoding phase, the diffusion process of the conditional diffusion model (CDM) utilizes compressed representations as potential conditions to guide the model in progressively reconstructing images over multiple time steps. Experimental findings demonstrate that the bi-residual compression network achieves superior peak signal-to-noise ratio (PSNR) across nine datasets, with an improvement of approximately 1 dB over SOTA methods. Moreover, BRC-CDM attains a PSNR that surpasses that of the majority of current methodologies, while also delivering enhanced spectral fidelity and perceptual quality. To foster reproducibility and further development, we release the full implementation of BRC-CDM at https://github.com/Nicle-L/BRC-CDM.