Ëàáîðàòîðèÿ êîìïüþòåðíîé ãðàôèêè è ìóëüòèìåäèà

This research aims to sufficiently increase the quality of visual-attention modeling to enable practical applications. We found that automatic models are significantly worse at predicting attention than even single-observer eye tracking. We propose a semiautomatic approach that requires eye tracking of only one observer and is based on time consistency of the observer's attention. Our comparisons showed the high objective quality of our proposed approach relative to automatic methods and to the results of single-observer eye tracking with no postprocessing. We demonstrated the practical applicability of our proposed concept to the task of saliency-based video compression.