Off-line Foveated Compression and Scene Perception: An Eye-Tracking Approach

With the continued growth of digital services offering storage and communication of pictorial information, the need to efficiently represent this information has become increasingly important, both from an information theoretic and a perceptual point of view. There has been a recent interest to design systems for efficient representation and compression of image and video data that take the features of the human visual system into account. One part of this thesis investigates whether knowledge about viewers' gaze positions as measured by an eye-tracker can be used to improve compression efficiency of digital video; regions not directly looked at by a number of previewers are lowpass filtered. This type of video manipulation is called off-line foveation. The amount of compression due to off-line foveation is assessed along with how it affects new viewers' gazing behavior as well as subjective quality. We found additional bitrate savings up to 50% (average 20%) due to off-line foveation prior to compression, without decreasing the subjective quality. In off-line foveation, it would be of great benefit to algorithmically predict where viewers look without having to perform eye-tracking measurements. In the first part of this thesis, new experimental paradigms combined with eye-tracking are used to understand the mechanisms behind gaze control during scene perception, thus investigating the prerequisites for such algorithms. Eye-movements are recorded from observers viewing contrast manipulated images depicting natural scenes under a neutral task. We report that image semantics, rather than the physical image content itself, largely dictates where people choose to look. Together with recent work on gaze prediction in video, the results in this thesis give only moderate support for successful applicability of algorithmic gaze prediction for off-line foveated video compression

Semantic Override of Low-level Features in Image Viewing – Both Initially and Overall

Guidance of eye-movements in image viewing is believed to be controlled by stimulus driven factors as well as viewer dependent higher level factors such as task and memory. It is currently debated what proportions these factors contribute to gaze guidance, and also how they vary over time after image onset. Overall, the unanimity regarding these issues is surprisingly low and there are results supporting both types of factors as being dominant in eye-movement control under certain conditions. We investigate how low, and high level factors influence eye guidance by manipulating contrast statistics on images from three different semantic categories and measure how this affects fixation selection. Our results show that the degree to which contrast manipulations affect fixation selection heavily depends on an image’s semantic content, and how this content is distributed over the image. Over the three image categories, we found no systematic differences between contrast and edge density at fixated location compared to control locations, neither during the initial fixation nor over the whole time course of viewing. These results suggest that cognitive factors easily can override low-level factors in fixation selection, even when the viewing task is neutral

The new man and the new world the influence of Renaissance humanism on the explorers of the Italian era of discovery

In contemporary research, microsaccade detection is typically performed using the calibrated gaze-velocity signal acquired from a video-based eye tracker. To generate this signal, the pupil and corneal reflection (CR) signals are subtracted from each other and a differentiation filter is applied, both of which may prevent small microsaccades from being detected due to signal distortion and noise amplification. We propose a new algorithm where microsaccades are detected directly from uncalibrated pupil-, and CR signals. It is based on detrending followed by windowed correlation between pupil and CR signals. The proposed algorithm outperforms the most commonly used algorithm in the field (Engbert & Kliegl, 2003), in particular for small amplitude microsaccades that are difficult to see in the velocity signal even with the naked eye. We argue that it is advantageous to consider the most basic output of the eye tracker, i.e. pupil-, and CR signals, when detecting small microsaccades