Reference number : GC-2017-Q4-01984, amendment number : , agreement type : G, recipient legal name : Zhao, Jiying (University of Ottawa)|Zhao, Jiying (Université d’Ottawa), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : The overall objective of this Engage project is to improve digital video wireless communication experience by_x000D_
utilizing deep learning based super-resolution. This goal will be achieved by developing deep learning based_x000D_
algorithms for video down-sampling at transmitter and video super-resolution at receiver. Moreover, we will_x000D_
use new deep learning methods to optimize the entire parameter selection process of a video encoder based on_x000D_
frame data and wireless link quality to achieve the best rate-distortion performance. The deep learning based_x000D_
down-sampling and super-resolution algorithms will be able to generate high resolution videos under_x000D_
dramatically changing communication bandwidth, significantly enhance the performance of video encoding for_x000D_
wireless communication, and therefore provide high quality videos under harsh wireless communication_x000D_
environments such as in forest, underground and in dense cities. The research achievement will definitely_x000D_
contribute to video super-resolution and wireless video transmission, which will benefit Canadian wireless_x000D_
industry in huge vertical markets such as video monitoring and surveillance, remote healthcare and smart_x000D_
senior homes, public safety and environment monitoring., program purpose french : The overall objective of this Engage project is to improve digital video wireless communication experience by_x000D_
utilizing deep learning based super-resolution. This goal will be achieved by developing deep learning based_x000D_
algorithms for video down-sampling at transmitter and video super-resolution at receiver. Moreover, we will_x000D_
use new deep learning methods to optimize the entire parameter selection process of a video encoder based on_x000D_
frame data and wireless link quality to achieve the best rate-distortion performance. The deep learning based_x000D_
down-sampling and super-resolution algorithms will be able to generate high resolution videos under_x000D_
dramatically changing communication bandwidth, significantly enhance the performance of video encoding for_x000D_
wireless communication, and therefore provide high quality videos under harsh wireless communication_x000D_
environments such as in forest, underground and in dense cities. The research achievement will definitely_x000D_
contribute to video super-resolution and wireless video transmission, which will benefit Canadian wireless_x000D_
industry in huge vertical markets such as video monitoring and surveillance, remote healthcare and smart_x000D_
senior homes, public safety and environment monitoring., agreement title english : Deep learning based super-resolution for video codec in wireless communication, agreement title french : Deep learning based super-resolution for video codec in wireless communication, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-03-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada