In order to predict human fixations we proposed a new approach by computing a saliency map of the difference of two images with different noise levels. These findings revealed that the non-textured and brightest areas are the most fixated and the most used to detect the presence of noise. Finally, we investigated the effects of scenes and textures on perceptual threshold and fixation paths. The comparison of the results showed that there was no significant difference between the thresholds measured in the different conditions. We implemented an online study and collected data in both conditions (laboratory, online). Ecological studies in image quality research are needed to understand noise perception under real-world conditions. Our results revealed that participants are using primarily their most central vision to detect a degradation in image quality. In a second task, observers were asked to detect a quality difference using only their peripheral vision. The perceptual threshold at 50% was obtained from the estimated psychometric function. In our first study, we varied the noise level of a part of the scene using the adaptive method Quest+. The conventional paradigms used in visual search and scene viewing tasks are not well suited to measure noise perception because the definition of noise is an unfamiliar concept to naive participants. However, investigating noise perception creates some methodological challenges. Our research aims at better understanding the human perception of this noise to optimize the computation time without detectable loss of image quality. The physically-based rendering using the Monte Carlo method to produce these images induces the presence of visual noise which decreases when the computation time increases.
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Thus, processing visuospatial information automatically activates the manual motor system, but the timing and direction of this effect vary depending on the type of stimulus.Ĭomputer-generated images are now commonly used in printed or electronic media.
The effect was stronger and appeared earlier for lateralized objects (60 ms after stimulus presentation) than for arrows (100 ms) or words (250 ms). Surprisingly, words led to the opposite pattern: larger force increase in the contralateral hand and smaller force increase in the ipsilateral hand. There was an early interaction between the presentation side or arrow direction and grip force: lateralized objects and central arrows led to a larger increase of the ipsilateral force and a smaller increase of the contralateral force. Participants held two grip force sensors while being presented with lateralized stimuli (exogenous attentional shifts, Experiment 1), left- or right-pointing central arrows (endogenous attentional shifts, Experiment 2), or the words "left" or "right" (endogenous attentional shifts, Experiment 3). A novel method with high temporal resolution–bimanual grip force registration–sheds light on this issue. However, it is unclear whether an explicit hand movement is necessary for this relationship to appear. Previous research demonstrated a close bidirectional relationship between spatial attention and the manual motor system.
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