Multi‐scale spatial‐spectral attention network for multispectral image compression based on variational autoencoder

•A hyperprior-based multiscale spatial-spectral attention network is proposed for multispectral image compression.•A neuroscience-based attention is combined with non-local meanstheory and local attention mechanism for spatially adaptive bits allocation.•A local multiscale channel attention is proposed to suppress less informative channels for spectrally adaptive bits allocation.•Scale-only hyperprior can make a better trade-off between complexity and performance in multispectral image compression. Based upon the fact that multispectral image compression needs to remove both spatial and spectral redundancy, recent learnt models via end-to-end manners have shown promising performance. However, most of them ignore the characteristics of multispectral image, i.e., the non-stationarity of spectral correlation and the scale-diversity of spatial features. Meanwhile, they directly utilize fully factorized entropy model, rendering compression performance suboptimal. This paper proposes a Multi-Scale Spatial-Spectral Attention Network (MSSSA-Net) based on variational autoencoder (VAE). Our MSSSA-Net (1) incorporates a simple neuroscience-based non-local attention module into attention mechanism to capture the tiny features in adjacent pixels and large-scale features in spatial domain simultaneously, (2) proposes a multi-scale spectral attention block to extract non-stationary correlation of adjacent spectra at different scales. We demonstrate that our MSSSA-Net offers the state-of-the-art performance in comparison with classical algorithms, including JPEG2000 and 3D-SPIHT, and recent learnt image compression models, on 7-band and 8-band datasets from Landsat-8 and WorldView-3 satellites, when measured by PSNR, MS-SSIM and Mean Spectral Angle. Extensive ablation experiments have verified the effectiveness of each component, and have demonstrated that, for multispectral image compression, Scale-only Hyperprior can make a better trade-off between compression performance and complexity compared with Mean & Scale Hyperprior and Joint Autoregressive model.