IBM System G Media & Entertainment Solution
Interactive and Semantic Tennis Video Experience
We created a new way to enjoy sports videos. Tennis videos are used as an example to implement a viewing program called 'Tennis Video 2.0'. Machine automatically executes content annotation, structure analysis, content extraction, and enriched rendering. There are three applications -- Structure, Interactivity, and Scalability. 'Structure' allows people to browse game videos and watch highlights on demands. Furthermore, it strategy search is a convenient way to find favorite hit patterns. 'Interactivity' provides people with functions to watch enriched game video rendered in real-time. These functions can provide more enjoyment for viewers. Scalability is for mobile use. We will discuss that in the Mobile section.enables This sports video viewer shall allow people enjoy watching games in many possible different ways, and also allow advertisers and content providers several different ways to interact with customers.
Tennis RealPlay (TRP) is an interactive tennis game system constructed with models extracted from real match videos. It has sensing compoents (using Wii, Kinect, or camera) to capture a person's behavior and integrate the person into the real game. You can imagine you control and play Federer in the US Open!!
The key techniques of TRP include player modeling and video-based player/court rendering. For player model creation, we propose a database normalization process and a behavioral transition model of tennis players, which might be a good alternative for motion capture in the conventional video games. For player/court rendering, we propose a framework for rendering vivid game characters and providing the real-time ability. We can say that image-based rendering leads to a more interactive and realistic rendering. Experiments show that video games with vivid viewing effects and characteristic players can be generated from match videos without much user intervention. Because the player model can adequately record the ability and condition of a player in the real world, it can then be used to roughly predict the results of real tennis matches in the next days. The results of a user study reveal that subjects like the increased interaction, immersive experience, and enjoyment from playing TRP.
Mobile Semantic Scalable Video
Scalable video is the research topic to provide different size of video bitstream under different transmission bandwidth. In this paper, the semantic scalability is proposed that provides the scalable videos in semantic domain, and the tennis videos are used as the experiments. Contrary to decreasing the video quality to reduce the bitrates, the lower bitstream size is achieved by abandoning the video contents with less semantic importance. The experimental results show that the proposed semantic scalability provides four levels of the scalable videos and maintains the visual quality in watching the game video. For user study, evaluators identify the visual quality of semantic scalability is more acceptable and the game information is clearer than Scalable Video Coding. The proposed scalability in semantic domain provides a new aspect for the scalable video.