A channel-gained single-model network with variable rate for multispectral image compression in UAV air-to-ground remote sensing

Unmanned aerial vehicle (UAV) air-to-ground remote sensing technology, has the advantages of long flight duration, real-time image transmission, wide applicability, low cost, and so on. To better preserve the integrity of image features during transmission and storage, and improve efficiency in the meanwhile, image compression is a very important link. Nowadays the image compressor based on deep learning framework has been updating as the technological development. However, in order to obtain enough bit rates to fit the performance curve, there is always a severe computational burden, especially for multispectral image compression. This problem arises not only because the complexity of the algorithm is deepening, but also repeated training with rate-distortion optimization. In this paper, a channel-gained single-model network with variable rate for multispectral image compression is proposed. First, a channel gained module is introduced to map the channel content of the image to vector domain as amplitude factors, which leads to representation scaling, as well as obtaining the image representation of different bit rates in a single model. Second, after extracting spatial-spectral features, a plug-and-play dynamic response attention mechanism module is applied to take good care of distinguishing the content correlation of features and weighting the important area dynamically without adding extra parameters. Besides, a hyperprior autoencoder is used to make full use of edge information for entropy estimation, which contributes to a more accurate entropy model. The experiments prove that the proposed method greatly reduces the computational cost, while maintaining good compression performance and surpasses JPEG2000 and some other algorithms based on deep learning in PSNR, MSSSIM and MSA.