Subjective assessment of 3D still images using attention models

The subjective process associated with image quality evaluation is endorsed by human psychophysical and physiological measurements. In the human visual system (HVS), the visual attention (VA) is a crucial element, which quickly identifies the notable regions of the images, subjectively linked to Regions of Interest (ROI). These are represented by a binary mask, indicating whether a pixel in the corresponding image belongs to the ROI. Subjective mechanisms dominate eye movements in the first two seconds of viewing and, due to the high relation between the eye actions and the VA, eye-tracking tests are used to validate 2D attention models: eye movements are recorded and processed to generate a Fixation Density Map (FDM). In the 3D domain, an essential factor for VA among the additional parameters of visual dimension is the scene depth. The main objective of this work was to study the impact of a particular ROI on the subjective quality perception of 3D still images, considering different types and level of noise (or distortion) in and out of the ROI. The 3DGaze images and eye movement database, obtained from an eye tracking experiment described in [1] and specifically created for performance evaluation of stereoscopic 3D attention models was used. Besides the full public availability, this database has original images in various HD sizes, all in PNG format and with natural content. Binary masks were generated for each FDM and different types and intensities of noise was added to each corresponding image according to the ROIs. By using the generated binary masks and image pixels positions in the ROI, the noise was added in the image regions located inside or outside the ROI. Subjective testing with users observing the images and scoring their quality was done to verify the importance of these regions in the subjective quality evaluation. The images were classified according to whether the noise was added inside or outside the ROI, the noise type (Gaussian, Speckle), parameter values (intensity level) and the noisy image view (left or right). The results have shown that Gaussian noise has less impact on the quality than Speckle, with higher intensity level and also when the noise is added to the right view and inside the ROI. This is justified due to the fact that viewers fix their eyes over the ROI during more time, thus perceiving higher distortion. As the amount of different image content was small, the information about the dominant eye appears to be inconclusive. Changing some parameters is suggested for future works, in such a way that there is more certainty on the results without interferences between analyses. As this work was limited to data contained in the image (bottom-up visual interest), some concepts related to the visualization context (top-down visual interest), such as rarity or surprise, may naturally be included in future works, as well as the characteristic of looking primarily for human faces or humanoid things