Video Retargeting: Automating Pan and Scan

Feng Liu and Michael Gleicher

Computer Sciences Department, University of Wisconsin-Madison

When a video is displayed on a smaller display than originally intended, some of the information in the video is necessarily lost. In this paper, we introduce Video Retargeting that adapts video to better suit the target display, minimizing the important information lost. We define a framework that measures the preservation of the source material, and methods for estimating the important information in the video. Video retargeting crops each frame and scales it to fit the target display. An optimization process minimizes information loss by balancing the loss of detail due to scaling with the loss of content and composition due to cropping. The cropping window can be moved during a shot to introduce virtual pans and cuts, subject to constraints that ensure cinematic plausibility. We demonstrate results of adapting a variety of source videos to small display sizes.

Experiment on home video. A clip from a digital camera is retargeted. Despite no identifiable faces and jerky handheld motion, the system still correctly applies a pan. Top: range of pan shown on source frames. Bottom: frames from retargeted result. Please refer to the paper for more examples. (Experiments on movies are omitted in this page for the copyright issue)

Feng Liu and Michael Gleicher. Video Retargeting: Automating Pan and Scan. ACM Multimedia 2006,Santa Barbara, CA, October 2006.pp.241-250. PDF
Video demo: 1, 2