In the digital point-and-shoot photography paradigm, users typically point the camera at the subjects, adjust framing, and trigger functions such as autofocus in preparation for taking a picture. This dissertation explores the opportunities for expanding the outcome of the point-and-shoot photography process by utilizing additional information collected while the user is framing a picture.

We propose a novel framework for digital photography that explores contextual information gathered while a photographer is composing and framing a picture. We call the information given by a video taken from the camera's viewpoint while framing, accompanied by per-frame capture parameters and inertial sensor data, the composition context of a photograph. In practice, this video can be recorded by saving the images displayed at the viewfinder during framing.

We started our investigation by conducting an exploratory study with users to gather composition context data in real-world photographic situations. In the study, we collected a large database of pictures with associated composition context data. An analysis of this dataset confirms that a significant portion of the composition context is highly correlated with the picture taken. In other words, part of the composition context can be understood as imaging the same scene pictured in the photograph with variations in capture parameters, such as field of view, exposure time, and focus.

By exploring this correlation and variability, we then show how computer vision and computational photography techniques can be applied to provide a wide range of interesting and compelling photo suggestions as a result of the picture-taking process. These include image composites, such as panoramas and high dynamic range images, and individual frames selected from the composition context.

We demonstrate that our framework can be integrated into new camera functionality. A "composition context camera" preserves the interface of current point-and-shoot cameras, but it uses the information in the composition context to compute additional interesting photo choices. We expect this capability to expand the photographic possibilities for casual and amateur users, who often rely on automatic camera modes.