Deep learning based super-resolution for video codec in wireless communication

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The overall objective of this Engage project is to improve digital video wireless communication experience byutilizing deep learning based super-resolution. This goal will be achieved by developing deep learning basedalgorithms for video down-sampling at transmitter and video super-resolution at receiver. Moreover, we willuse new deep learning methods to optimize the entire parameter selection process of a video encoder based onframe data and wireless link quality to achieve the best rate-distortion performance. The deep learning baseddown-sampling and super-resolution algorithms will be able to generate high resolution videos underdramatically changing communication bandwidth, significantly enhance the performance of video encoding forwireless communication, and therefore provide high quality videos under harsh wireless communicationenvironments such as in forest, underground and in dense cities. The research achievement will definitelycontribute to video super-resolution and wireless video transmission, which will benefit Canadian wirelessindustry in huge vertical markets such as video monitoring and surveillance, remote healthcare and smartsenior homes, public safety and environment monitoring.