Learned Hyperspectral Compression Using a Student’s T Hyperprior

Abstract

Hyperspectral compression is one of the most common techniques in hyperspectral image processing. Most recent learned image compression methods have exhibited excellent rate-distortion performance for natural images, but they have not been fully explored for hyperspectral compression tasks. In this paper, we propose a trainable network architecture for hyperspectral compression tasks, which not only considers the anisotropic characteristic of hyperspectral images but also embeds an accurate entropy model using the non-Gaussian prior knowledge of hyperspectral images and nonlinear transform. Specifically, we first design a spatial-spectral block, involving a spatial net and a spectral net as the base components of the core autoencoder, which is more consistent with the anisotropic hyperspectral cubes than the existing compression methods based on deep learning. Then, we design a Student’s T hyperprior that merges the statistics of the latents and the side information concepts into a unified neural network to provide an accurate entropy model used for entropy coding. This not only remarkably enhances the flexibility of the entropy model by adjusting various values of the degree of freedom, but also leads to a superior rate-distortion performance. The results illustrate that the proposed compression scheme supersedes the Gaussian hyperprior universally for virtually all learned natural image codecs and the optimal linear transform coding methods for hyperspectral compression. Specifically, the proposed method provides a 1.51% to 59.95% average increase in peak signal-to-noise ratio, a 0.17% to 18.17% average increase in the structural similarity index metric and a 6.15% to 64.60% average reduction in spectral angle mapping over three public hyperspectral datasets compared to the Gaussian hyperprior and the optimal linear transform coding methods.

Remote Sensing Image Compression Based on the Multiple Prior Information

“…This extra information only occupies a small bit rate but can help construct a more accurate entropy model. This structure has significantly improved compression performance, and many later studies have adopted similar structures [10,42,44,47,50]. With the hyperprior information, the whole compression scheme can be written as…”

“…Experimental results on GF-2 data demonstrate that this algorithm achieves good compression performance. In [42], the researchers design a novel learned hyperspectral remote sensing image compression algorithm that incorporates a spectral attention mechanism and a novel entropy model based on Student's t-distribution. This algorithm achieves better compression performance on several hyperspectral image datasets.…”