Optimization of HVS based objective image quality assessment with eye tracking

In this paper, we present an approach to predict perceived quality of compressed images while incorporating real visual attention coordinates. Information about the visual attention is not usually taken into account in image quality assessment models. The idea of implementing gaze information into the image quality assessment system lies in that the artefacts are more disturbing to human observer in the region with higher saliency than in other parts of an image. Impact of the region of interest on estimation accuracy of a simple image quality metric is investigated. The gaze coordinates were calculated using calibrated electro-oculogram records of human observers while watching a number of test images. The same images were used for subjective image quality assessment. Obtained mean opinion scores of perceived image quality and eye tracking data were used to verify potential improvement of assessment accuracy for a simple image quality metric. Based on the proven effect, our previously developed system for still image quality assessment has been adapted while utilizing information about the visual attention. The potential performance improvement of existing image coding while incorporating the spatially adaptive HVS is discussed.