Subjective Video Quality Assessment in Segmentation for Augmented Reality Applications

Abstract

Video segmentation to extract a person in foreground has been a common task in many Augmented Reality (AR) applications. In natural environments which the background color and the light environment are not constant the segmentation method must be able to extract the element of the interest in these conditions. However, methods for segmentation in natural environment are more error prone than the traditional ones which are based on a constant color elimination. Thus, in order to use them in a large number of applications it is necessary to know how the segmentation errors are perceived by the users in order to focus on development of algorithms which avoid the more perceptible ones. In this work a video quality subjective assessment method was applied to obtain the AR user's opinions about videos with different misclassified pixels rates. The results showed that segmentation errors are perceived by AR applications users. However, the video quality was not related with the number of the misclassified pixels. In addiction, it was noted that when the errors concentrated in the element of the interest increase the score of the associated video decreases.