Shrinkability Maps for Content‐Aware Video Resizing
Yifei Zhang, Shi‐Min Hu, Ralph Robert Martin · Computer Graphics Forum · 2008
Abstract A novel method is given for content‐aware video resizing, i.e. targeting video to a new resolution (which may involve aspect ratio change) from the original. We precompute a per‐pixel cumulative shrinkability map which takes into account both the importance of each pixel and the need for continuity in the resized result. (If both x and y resizing are required, two separate shrinkability maps are used, otherwise one suffices). A random walk model is used for efficient offline computation of the shrinkability maps. The latter are stored with the video to create a multi‐sized video, which permits arbitrary‐sized new versions of the video to be later very efficiently created in real‐time, e.g. by a video‐on‐demand server supplying video streams to multiple devices with different resolutions. These shrinkability maps are highly compressible, so the resulting multi‐sized videos are typically less than three times the size of the original compressed video. A scaling function operates on the multi‐sized video, to give the new pixel locations in the result, giving a high‐quality content‐aware resized video. Despite the great efficiency and low storage requirements for our method, we produce results of comparable quality to state‐of‐the‐art methods for content‐aware image and video resizing.