A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attention-based adaptive intra refresh for error-prone video transmission
IEEE Communications Magazine
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Packets in a video bitstream contain data with different levels of importance that yield unequal amounts of quality distortion when lost. In order to avoid sharp quality degradation due to packet loss, we propose in this paper an error resilience method that is applied to the Region of Interest (RoI) of the picture. This method protects the RoI while not yielding significant overhead. We perform an eye tracking test to determine the RoIs of a video sequence and we assess the performance of the proposed model in error-prone environments by means of a subjective quality test. Loss simulation results show that stopping the temporal error propagation in the RoIs of the pictures helps preserving an acceptable visual quality in the presence of packet loss.