Abstract
Online live streaming has been widely used in distant teaching, online live shopping, and so on. Particularly, online teaching live streaming breaks the time and space boundary of teaching and has better interactivity, which is a new distant education mode. As a new online sales model, online live shopping promotes the rapid development of Internet economy. However, the quality of live video affects the user experience. This paper studies the optimization algorithm of ultra-high-definition live streaming, focusing on superresolution technology. Convolutional neural network (CNN) is a multilayer artificial neural network designed to process two-dimensional input data. It takes advantage of CNN in image processing. This paper proposes an image superresolution algorithm based on hybrid dilated convolution and Laplacian pyramid. By mixing the dilated convolution module, the receptive field of the network can be improved more effectively to obtain more context information so that the high-frequency features of the image can be extracted more effectively. Experiment was running on Set5, Set14, Urban100, and BSD100 datasets, and the results reveal that the proposed algorithm outperforms baselines with respect to peak signal to noise ratio (PSNR), structural similarity index measurement (SSIM), and image quality.
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