一种面向视频传输应用的联合上下采样超分辨率框架

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

针对目前面向视频传输应用中,对低分辨率视频应用超分辨率技术进行还原时引发严重病态性问题,结合视频传输的全过程,提出一种联合上下采样的超分辨率框架.该框架通过将下采样过程和超分辨率过程进行联合训练,使得原始高分辨率视频的信息能够指导低分辨率视频的重建,并且下采样过程和超分辨率过程互相约束,减小了整个映射空间的尺寸,使得模型的泛化能力更强.实验表明,提出的方法在常用的图像超分辨率数据集上峰值信噪比(peak signal-to-noise ratio,PSNR)指标平均提升超过2.9 dB,在国际视频编码标准HEVC标准测试序列上平均达到近乎无损(PSNR指标超过40 dB),证明所提框架对于视频传输应用具有积极的意义.

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.