From night vision goggles, to infrared imagers, to remote controlled bomb disposal robots; we are increasingly employing electronic vision sensors to extend or enhance the limitations of our own visual sensory system. And while we can make these systems better in terms of the amount of power they use, how much information they capture, or how much information they can send to the viewer, it is also important to keep in mind the capabilities of the human who must receive this visual information from the sensor and display system. The best interface between our own visual sensory system and that of the electronic image sensor and display system is one where the least amount of visual information is sent to our own sensory system for processing, yet contains all the visual information that we need to understand the desired environment and to make decisions based on that information. In order to do this it is important to understand both the physiology of the visual sensory system and the psychophysics of how this information is used. We demonstrate this idea by researching and designing the components needed to optimize the compression of dynamic range information onto a display, for the sake of maximizing the amount of perceivable visual information shown to the human visual system.
An idea that is repeated in the construction of our optimal system is the link between designing, modeling, and validating both the design and the model through human testing. Compressing the dynamic range of images while trying to maximize the amount of visual information shown is a unique approach to dynamic range cornpression. So the first component needed to develop our optimal compression method is a way to measure the amount of visual information present in a compressed image. We achieve this by designing an Information Content Quality Assessment metric and we validate the metric using data from our psychophysical experiments [in preparation]. Our psychophysical experiments compare different dynamic range compression methods in terms of the amount of information that is visible after compression. Our quality assessment metric is unique in that it models visual perception using information theory rather than biology. To validate this approach, we extend our model so that it can predict human visual fixation. We compare the predictions of our model against human fixation data and the fixation predictions of similar state of the art fixation models. We show that the predictions of our model are at least corn-parable or better than the predictions of these fixation models. We also present preliminary results on applying the saliency model to identify potentially salient objects in out-of-focus locations due to a finite depth-of-field [in preparation]. The final component needed to implement the optimization is a way to combine the quality assessment model with the results of the psychophysical experiments to reach an optimal compression setting. We discuss how this could be implemented in the future using a generic dynamic range compression algorithm. We also present the design of a wide dynamic range image sensor and a mixed mode readout scheme to improve the accuracy of illumination measurements for each pixel over the entire dynamic range of the imager.
|Commitee:||Andreou, Andreas, Elhilali, Mounya|
|School:||The Johns Hopkins University|
|Department:||Electrical and Computer Engineering|
|School Location:||United States -- Maryland|
|Source:||DAI-B 74/12(E), Dissertation Abstracts International|
|Subjects:||Computer Engineering, Electrical engineering|
|Keywords:||Electronic vision sensors, Human vision, Information content capture, Visual information|
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