Journal of Information Technology & Software Engineering
Open Access

How does image noise affect viewer s visual attention and scene perception?

2^nd Global Summit and Expo Multimedia & Applications

August 15-16, 2016 London, UK

Kun Guo

University of Lincoln, UK

Posters & Accepted Abstracts: J Inform Tech Softw Eng

Abstract :

A central research question in natural vision is how to allocate fixation to extract informative cues for scene perception. With high quality images, psychological and computational studies have made significant progress to understand and predict human gaze allocation in scene exploration and understaidng. However, it is unclear whether these findings can be generalised to degraded naturalistic visual inputs. Here, we combined psychophysical, eye-tracking and computational approaches to systematically examine the impact of image resolution and image nosie (Gaussian low-pass filter, circular averaging filter, Additive Gaussian white noise) on observers��? gaze allocation and subsequent scene perception when inspecting both manmade and natural scenes. Compared with high quality images, degraded scenes would reduce the perceived image quality and affect the scene categorization, but this deterioration effect was scene content-dependent. Distorted images also attracted fewer numbers of fixations but longer fixation durations, shorter saccade distance and stronger central fixation bias. The impact of image noise manipulation on gaze distribution was mainly determined by noise intensity rather than noise type, and was more pronounced for natural scenes than for man-made scenes. We further compared 4 high performing visual attention models in predicting human gaze allocation in degraded scenes, and found that model performance lacked human-like sensitivity to noise type and intensity, and was considerably worse than human performance measured as inter-observer variance. Our results indicate a crucial role of external noise intensity in determining scene-viewing gaze behaviour and scene understanding, which should be considered in the development of realistic human-vision-inspired attention models.

Biography :

Email: kguo@lincoln.ac.uk