A Cloud Computing Based Deep Compression Framework for UHD Video Delivery

Ultra-high-definition (UHD) videos are enjoying increased popularity in people's daily usage because of the good visual experience. However, the data size of UHD videos is 4-16 times larger of HD videos. This will bring many challenges to existing video delivery systems, such as the shortage of network bandwidth resources and longer network transmission latency. In this article, we propose a cloud computing based deep compression framework named Pearl, which utilizes the power of deep learning and cloud computing to compress UHD videos. Pearl compresses UHD videos from two respects: the frame resolution and the colorful information. In pearl, an optimal compact representation of the original UHD video is learned with two deep convolutional neural networks (DCNNs): super resolution CNN (SR-CNN) and colorization CNN (CL-CNN). SR-CNN is used to reconstruct a high resolution video from a low resolution video while CL-CNN is adopted to preserve the color information of the video. Pearl focuses on video content compression in two new directions. Thus, it can be integrated with any existing video compression system. With Pearl, the data size of UHD videos can be significantly reduced. We evaluate the performance of Pearl with a wide variety of network conditions, quality of experience (QoE) metrics, and video properties. In all considered scenarios, Pearl can further compress 84% of video size and reduce 73% of network transmission latency.