Online communities and social networks are the most popular sites on the Internet, and have exploded with multimedia content in the last decade. Most web designers recognize that site images can be saved with lower fidelity to reduce bandwidth consumption and increase capacity, though many are reluctant to do so for aesthetic concerns. However, there are many images that site designers have little direct control over---socially sourced images. Many social networks automatically reduce the fidelity of uploaded images in order to conserve bandwidth. Social networks also contain a vast archive of images with popularity indicators, such as likes and shares, which recent work has correlated with psychovisual features within the images. In this paper, we investigate the trade-off between fidelity reduction and selected psychovisual enhancements. We demonstrate that even simple enhancements can be used to enable more aggressive optimization of socially sourced content, which has implications for static content delivery networks and image servers. Through user testing on real images, we validate the efficacy of our proposed approach.
['Aaron Koehl', 'Haining Wang']
SERF: optimization of socially sourced images using psychovisual enhancements