Joint Up-and-Down Sampling Super-resolution Framework for Video Transmission Application Services

In view of the serious ill-posed problems caused by the application of super-resolution technology to low-resolution video in current video transmission applications, a joint up-and-down sampling super-resolution framework combined with the whole process of video transmission was proposed in this paper. Through the joint training of the down-sampling process and the super-resolution process, the information of the original high-resolution video could guide the reconstruction of low-resolution video, and the down-sampling process and the super-resolution process were mutually constrained, which reduced the size of the whole mapping space and made the generalization ability of the model stronger. Experiments show that the proposed method improves the peak signal-to-noise ratio (PSNR) index on average by more than 2.9 dB of the commonly used image super-resolution data set, and reaches nearly lossless on average on the international video coding standard HEVC test sequence (the PSNR index exceeds 40 dB). The results prove that the framework proposed in this paper has positive significance for video transmission application service.