Deep learning-based image compression methods have led to high rate-distortion performances compared to traditional codecs. Recently, Generative Adversarial Networks (GANs)-based compression models, e.g., High Fidelity Compression (HiFiC), have attracted great attention in the computer vision community. However, most of these works aim for spatial compression only and do not consider the spatio-spectral redundancies observed in hyperspectral images (HSIs). To address this problem, in this paper, we adapt the HiFiC spatial compression model to perform spatio-spectral compression of HSIs. To this end, we introduce two new models: i) HiFiC using Squeeze and Excitation (SE) blocks (denoted as HiFiC$_{SE}$); and ii) HiFiC with 3D convolutions (denoted as HiFiC$_{3D}$). We analyze the effectiveness of HiFiC$_{SE}$ and HiFiC$_{3D}$ in exploiting the spatio-spectral redundancies with channel attention and inter-dependency analysis. Experimental results show the efficacy of the proposed models in performing spatio-spectral compression and reconstruction at reduced bitrates and higher reconstruction quality when compared to JPEG 2000 and the standard HiFiC spatial compression model. The code of the proposed models is publicly available at https://git.tu-berlin.de/rsim/HSI-SSC .

翻译：基于深度学习的图像压缩方法相较于传统编解码器在率失真性能上取得了显著提升。近年来，基于生成对抗网络（GANs）的压缩模型（如高保真压缩HiFiC）在计算机视觉领域引起了广泛关注。然而，现有研究大多仅针对空间压缩，未能充分考虑高光谱图像（HSIs）中存在的时空-光谱冗余。为解决此问题，本文对HiFiC空间压缩模型进行改进，以实现HSI的时空-光谱联合压缩。为此，我们提出两种新模型：i）引入Squeeze and Excitation（SE）模块的HiFiC（记为HiFiC$_{SE}$）；ii）采用3D卷积的HiFiC（记为HiFiC$_{3D}$）。通过通道注意力及互依赖性分析，我们探讨了HiFiC$_{SE}$与HiFiC$_{3D}$在利用时空-光谱冗余方面的有效性。实验结果表明，与JPEG 2000及标准HiFiC空间压缩模型相比，所提模型能在更低比特率下实现时空-光谱压缩与重建，并取得更优的重建质量。模型代码已公开于https://git.tu-berlin.de/rsim/HSI-SSC。