Glyph : lightweight and evocative looping images in the news

Abstract

There is an emotional dimension to the informative function of the news. When we read the news we participate in a collective emotional experience- whether that is grief, celebration, worry, or wonder. News video is a crucial vector for these shared emotional experiences, which can propel civic action. But video comes at a high cost in time and attention, and is thus unsuitable for high volume news and social feeds, and mobile and wearable devices. On these interfaces, there is value in presenting video in a way that's immediately evocative, preserving the richness of video while atomizing it to an excerpt as "glanceable" as a still image. This thesis proposes Glyph, a tool for creating expressive, seamlessly-looping GIFs from video. The tool integrates opensource software for video/image manipulation and loop detection into a simple, web-based authoring interface. Glyph allows a user to automatically detect perfect loops that occur in a video, create the appearance of seamless motion in a non-looping clip, still some regions of movement in a clip to highlight others, or imbue a still frame with subtle dynamism. The part-automated, part-manual editing tool thus allows users to quickly build nonliteral excerpts from video that can immediately crystalize an affective quality or crucial moment, suspending and intensifying its semantic or emotional content through continuous, seamless looping. This thesis additionally explores applications for this class of image, called glyphs.