Discrete Wavelet Transform-Based Gaussian Mixture Model for Remote Sensing Image Compression

High-ratio image compression is difficult because remote sensing images have complex backgrounds and rich information, and the correlation between features is weak. An accurate entropy model is an important way to solve the problem by enhancing the representation ability of the compression models. The entropy model is more suited to estimate the probability distributions with the sparse latent representations. This study proposes a novel entropy model [discrete wavelet transform Gaussian mixture model (DWTGMM)] based on discrete wavelet transform (DWT) and Gaussian mixture model (GMM) for remote sensing image compression. The method uses DWT to transform the latent representations into a wavelet domain and obtains four sparse representations, and then uses the proposed DWTGMM to model them separately to estimate the probability distribution of each element. It is noteworthy that the DWT used in our approach does not require learning parameters and can be combined with other entropy models to acquire the distribution of latent representations. To evaluate our method, we construct three remote sensing image datasets, i.e., GoogleMap, GF1, and GF7. We compare our method with several popular learned compression models and traditional codecs. Experimental results show that the proposed method can achieve excellent performance with low complexity. Especially with the same model architecture, the DWTGMM achieves the best compression performance.