Sometimes, when we watch a video on the internet, the resolution of the video becomes bad depending on the network connection status. This phenomenon happens because we use the adaptive streaming method, which adjusts the streaming video resolution in real time following the ever-changing internet bandwidth. To solve this problem, prof. Jinwoo Shin and Dongsu Han research group have recently developed the technology that improves the quality of video regardless of network status. This technology has developed by combining ‘adaptive streaming’ and ‘super-resolution technology’ based on deep learning.
The research group has input the low-resolution and high-resolution data repetitively using CNN (Convolution Neural Network), which is a kind of deep-learning technology. Based on the CNN, ‘super-resolution technology’, which is a method of delicately extending the horizontal and vertical scales on the display, was used. Especially, when streaming video, “super-resolution” realizing CNN can be downloaded together so that the method to improve resolution in real time regardless of network condition has proposed. In case of CNN file, downloading this with video is light work because its size is up to 2MB. In addition, because it was designed to download divided CNN file pieces in order, ‘super-resolution’ technology can be applied to video with only some parts of CNN file.
The research team has shown that state-of-art adaptive streaming resolution can be realized with 26.9% lowered internet bandwidth through this system and, explained that 40% higher quality can be supplied with same internet bandwidth through this system. Prof. Dongsu Han said that “this technology has applied to youtube, Netflix’s video transmission systems and it makes big meaning to have practicality.” In addition, “it’s only implemented on a desktop now, but we will continue to develop it to make it work on mobile devices in the future.”