A Channel Adaptive Encoding and Decoding Method for Unmanned Aerial Vehicle Image Transmission

Unmanned Aerial Vehicles (UAVs) are an indispensable core component of low altitude economic networks. It is very critical for achieving efficient UAV image transmission of air-to-ground communication. Due to the changes in flight area and unstable channel conditions, the signal-to-noise and transmission rate change rapidly. To adapt to these changes, we propose a channel adaptive encoding and decoding method for UAV image transmission. The proposed method includes a lightweight feature extraction module, a channel-wise feature enhance module, a transmission rate adaptive module, and the corresponding decoding module. The lightweight feature extraction module can quickly extract local detailed features and long-range spatial dependencies via residual block and mobile mamba. The channel-wise feature enhance module can enhance channel wise useful features via the involution operation and the SNR adjustment block according to channel state SNRs. The transmission rate adaptive module can further adaptively adjust the size of transmission features according to the transmission rate via the rate adjustment block and the rate mask block. The extensive experimental results on the SIRI-WHU, WHU-RS19, AID, and UCMerced Land Use datasets demonstrate that our method obtains higher PSNR, MS-SSMI, LPIPS and ACC than state-of-the-art methods.