Description
In 2016, Cisco released the White paper, VNI Forecast and Methodology 2015-2020, which predicted that by 2020, 82 percent of Internet traffic would come from video applications such as video surveillance and content delivery networks. The report also revealed that in 2015, Internet video surveillance traffic nearly doubled, virtual reality traffic quadrupled, TV grew by 50 percent, and other applications similarly saw significant increases. The report estimated that the annual global traffic would first time exceed the zettabyte (ZB; 1000 exabytes [EB]) threshold in 2016 and will reach 2.3 ZB by 2020, with 1.886 ZB attributed to video data.Today, Al and machine learning are increasingly being used in video processing to improve video quality, reduce bandwidth requirements, and enhance user experience. For instance, Al algorithms can optimize video encoding parameters based on the content of the video, reducing the bitrate required for a given level of video quality. Al can also be used for video content analysis, enabling automated scene detection, object recognition, and event detection. This has significant applications in video surveillance, where Al algorithms can be used to identify and track individuals or objects of interest in real-time.
Overall, the use of Al in video is a rapidly growing field with immense potential for improving the efficiency and quality of multimedia services. In this talk, I will present the latest research results on machine learning and deep neural network-based video coding, and their applications to the real world, such as saliency detection and underwater imaging.
| Period | 7 Dec 2023 |
|---|---|
| Event title | Distinguished Scholars Seminar Series 2023 |
| Event type | Seminar |
| Location | Hong KongShow on map |