Semiautomatic visual-attention modeling and its application to video compression

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.