A significant part of many videos of lectures is presentation slides that occupy much of the field of view. Further, for a student studying the lecture, having the slides sharply displayed is especially important, compared with the speaker, background, and audience. However, even if the original capture supports it, the bandwidth required for real time viewing is substantive, especially in the context of mobile devices. Here we propose reconstructing the video on the client side by backprojecting high resolution slide images into the video stream with the slide area blacked out. The high resolution slide deck can be sent once, and inserted into the video on the client side based on the transformation (a homography) computed in advance. We further introduce the idea that needed homography transformations can be approximated using affine transformations, which allows it to be done using built-in capabilities of HTML 5. We find that it is possible to significantly reduce bandwidth by compressing the modified video, while improving the slide area quality, but leaving the non-slide area roughly the same.

We present a system that assists users in viewing videos of lectures on small screen devices, such as cell phones. It automatically identifies semantic units on the slides, such as bullets, groups of bullets, and images. As the participant views the lecture, the system magnifies the appropriate semantic unit while it is the focus of the discussion. The system makes this decision based on cues from laser pointer gestures and spoken words that are read off the slide. It then magnifies the semantic element using the slide image and the homography between the slide image and the video frame. Experiments suggest that the semantic units of laser-based events identified by our algorithm closely match those identified by humans. In the case of identifying bullets through spoken words, results are more limited but are a good starting point for more complex methods. Finally, we show that this kind of magnification has potential for improving learning of technical content from video lectures when the resolution of the video is limited, such as when being viewed on hand held devices.