A Coupled Compression Generation Network for Remote-Sensing Images at Extremely Low Bitrates
Tianpeng Pan, Lili Zhang, Lele Qu, Yuxuan Liu · IEEE Transactions on Geoscience and Remote Sensing · 2023
Benefiting from the excellent texture recovery capability of generative adversarial networks (GANs), generated images are capable of maintaining clear texture features even when compressed into extremely low-bit streams. In recent years, the GAN has made great progress in extremely low-bit compression for natural images. However, a few studies have been conducted on extremely low-bit compression for the remote-sensing (RS) field. We find that a single GAN tends to generate visually pleasing texture information, and this characteristic may affect the visual effect and accuracy of other computer vision tasks. Therefore, we propose a coupled compression generation network (CCGN) that reconstructs the image content and detailed textures separately and fuses them to achieve a balanced image reconstruction task at extremely low bitrates for remote-sensing images. Specifically, a multidimensional residual attention mechanism (MRAM) is adopted to achieve extremely low-bit stream generation, whereas contentwise images and texturewise images are reconstructed using the same generator with different training strategies. We further optimize the texture generation strategy, and an enhanced perceptual-guided refinement stage (EPGRS) and a multiscale fusion discriminator (MSFD) are developed for a more realistic texture. The proposed method achieves outstanding results on compression tasks on the dataset for object detection in aerial images (DOTA), and the fused results of extremely low-bit streams also perform well in object detection tasks, significantly alleviating pressure from bandwidth and storage space.