Ultra-high-definition (UHD) videos has increased popularity. However, the data size of UHD videos is 4-16 times larger of HD videos. This brings many challenges to existing video delivery systems, such as the shortage of network bandwidth resources and longer network transmission latency. In this paper, 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. We evaluate the performance of Pearl with a wide variety of network conditions, quality of experience metrics, and video properties. In all considered scenarios, Pearl can further compress 84% of video size and reduce 73% of network transmission latency.