Hyperspectral Image Compression with Spectral-Spatial Coupling and Group-Wise Context Modeling

The rich spectral information within hyperspectral images (HSIs) results in large data volumes. Thus finding a compact representation for HSIs while maintaining reconstruction quality is a fundamental task for numerous applications. Though the existing learning-based compression methods and context models have shown strong rate-distortion (RD) performance, these methods only pay their attention on spatial redundancy without considering the spectral redundancy of HSIs, which thus impedes further improvement of their performance on HSI. Moreover, the strictly sequential autoregressive nature of context models leads to inefficiency, further limiting their practical applications. In this paper, leveraging the spectral priors unique to HSIs, we propose a hybrid Transformer-CNN architecture to find compact latent representations of HSIs. In specific, we construct Spectral-Spatial Coupling Transformer Group (SSCTG) to cooperatively extract spatial and spectral features of HSIs. Additionally, we propose Group-wise Context Model (GCM) to further enhance the parallel processing capability of autoregression within context models, significantly improving the coding efficiency. Extensive experiments demonstrate the effectiveness of the proposed method, achieving superior RD performance compared to state-of-the-art methods while maintaining high efficiency of codecs.