We propose a multicue gaze prediction framework for open signed video content, the benefits of which include coding gains without loss of perceived quality. We investigate which cues are relevant for gaze prediction and find that shot changes, facial orientation of the signer and face locations are the most useful. We then design a face orientation tracker based upon grid-based likelihood ratio trackers, using profile and frontal face detections. These cues are combined using a grid-based Bayesian state estimation algorithm to form a probability surface for each frame. We find that this gaze predictor outperforms a static gaze prediction and one based on face locations within the frame.

This paper proposes a gaze prediction model for open signed video content. A face detection algorithm is used to locate faces across each frame in both profile and frontal orientations. A grid-based likelihood ratio track before detect routine is used to predict the orientation of the signer's head, which allows the gaze location to be localised to either the signer or the inset. The face detections are then used to narrow down the gaze prediction further. The gaze predictor is able to predict the results of an eye tracking study with up to 95% accuracy, and an average accuracy of over 80%.

This article discusses a framework for model-based, context-dependent video coding based on exploitation of characteristics of the human visual system. The system utilizes variable-quality coding based on priority maps which are created using mostly context-dependent rules. The technique is demonstrated through two case studies of specific video context, namely open signed content and football sequences. Eye-tracking analysis is employed for identifying the characteristics of each context, which are subsequently exploited for coding purposes, either directly or through a gaze prediction model. The framework is shown to achieve a considerable improvement in coding efficiency.