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

基于深度学习的无线通信视频编解码器超分辨率

• 批准号: 522185-2017
• 负责人: Zhao, Jiying
• 金额: $ 1.82万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640109
• 关键词: Deep; learning; based; super; resolution

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.

该参与项目的总体目标是通过基于深度学习的超分辨率来改善数字视频无线通信体验。通过开发深度学习的基础算法来实现此目标，以用于在接收器的发射器和视频超分辨率上进行视频减速采样。此外，我们将采用新的深度学习方法，以优化基于框架数据和无线链接质量的视频编码器的整个参数选择过程，以实现最佳的利率延伸性能。基于深度学习的采样和超分辨率算法将能够产生高分辨率的视频，不断变化的通信粉刷，大大提高了视频编码的无效通信的性能，因此在诸如森林，地下和密集的密集城市等严酷的无线通信环境下提供了高质量的视频。研究成就无疑将与视频超分辨率和无线视频传输联系在一起，这将使在巨大的垂直市场（例如视频监视和监视，远程医疗保健和智能住宅，公共安全和环境监视）等巨大的垂直市场中受益。