Y: luminance Cb: chrominance-blue and Cr: chrominance-red Super-resolution generative adversarial networks Super-resolution convolutional neural networks Learned perceptual image patch similarity Super-resolution of compressed images using deep convolutional neural networksĮfficient sub-pixel convolutional neural networksĮnhanced super-resolution generative adversarial networks The following abbreviations are used in this manuscript: The traffic decrease reaches 98.42% in total. Using this model with added compression can decrease the quantity of data delivered to surrogate servers in a cloud video-delivery framework. According to our findings, the VSRGAN+ model can reconstruct videos without perceptual distinction of the ground truth. Additionally, we propose a cloud video-delivery framework that uses video super-resolution. We also test it on a large-scale JND-based coded video quality dataset containing 220 video clips with four different resolutions. We train our model with a dataset proposed to teach systems for high-level visual comprehension tasks. In this paper, we leverage the power of deep-learning-based super-resolution methods and implement a model for video super-resolution, which we call VSRGAN+. Consequently, they have generated traffic with an increasing quantity of data in network infrastructures, which continues to grow, e.g., global video traffic is forecast to increase from 75% in 2017 to 82% in 2022. In the last decade, video-streaming applications have also become popular. These models have been used to improve the quality of videos and images. In recent years, image and video super-resolution have gained attention outside the computer vision community due to the outstanding results produced by applying deep-learning models to solve the super-resolution problem.
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