Image Based Rendering (IBR) allows interactive scene exploration from images alone. However, despite considerable development in the area, one of the main obstacles to better quality and more realistic visualizations is the occurrence of visually disagreeable artifacts. In this paper we present a methodology to map out the perception of IBR-typical artifacts. This work presents an alternative to traditional image and video quality evaluation methods by using an EEG device to determine the implicit visual processes in the human brain. Our work demonstrates the distinct differences in the perception of different types of visual artifacts and the implications of these differences.

The way images are perceived by a human observer is becoming increasingly important in visual media, e.g. for (photo-) realistic image synthesis or memory-efficient encoding of large volumes of image/video data. Traditionally, perceived image quality is assessed by either using specially developed image quality metrics or by conducting user studies. In this paper, we investigate the use of electroencephalography as a tool for evaluating image quality. We demonstrate that the presence of artifacts reliably elicits a measurable response in the brain. We furthermore show that the reaction varies depending on the severity of artifacts, so that it may be used in order to objectively quantify image quality.

When new computer graphics algorithms for image and video editing, rendering or compression are developed, the quality of the results has to be evaluated and compared. Since the produced media are usually to be presented to an audience it is important to predict image and video quality as it would be perceived by a human observer. This can be done by applying some image quality metric or by expensive and time consuming user studies. Typically image quality metrics do not correlate to quality perceived by a human observer. Similarly a drawback of user studies is that perceived image or video quality is filtered by a decision process, which, in turn, is influenced by the performed task and chosen quality scale. To get an objective view on (subjectively) perceived image quality, electroencephalography can be used. In this paper we show that artifacts appearing in videos elicit a measurable brain response which can be analyzed using the event-related potentials technique. Since electroencephalography itself requires an elaborate procedure, we aim to find a minimal setup to reduce time and participants needed to conduct a reliable study of image and video quality. As a first step we demonstrate that the reaction to a video with or without an artifact can be identified by an off-the-shelf support vector machine, which is trained on a set of previously recorded responses, with a reliability of up to 80% from a single recorded electroencephalogram.