VISUAL ATTENTION PROBABILITY MODEL FOR STEREOSCOPIC VIDEOS ESTIMATED USING STATISTICAL DESIGN OF EXPERIMENTS

Abstract

Viewers of videos are likely to absorb more information from the part of the screen that attracts visual attention. This fact has lead to the visual attention models that is being used in producing and evaluating videos. In this paper, we investigate the factors that are significant to visual attention and the mathematical form of the visual attention model, and then estimate the visual attention probability using the statistical design of experiments. The analysis of variance (ANOVA) verifies that the motion velocity, distance from the screen, and amount of defocus blur are the factors that strongly affect human visual attention. Using the response surface modeling (RSM), we create a visual attention score model that concerns the three factors, and from which model we calculate the visual attention probabilities (VAPs) of image pixels. The VAPs are directly applied to existing gradient based 3D effect perception measurement. By giving weights according to our VAPs, more accurate measurement is possible. The performance of the proposed measurement is assessed by comparing them with subjective evaluation as well as with existing methods. The comparison verifies that the proposed measurement outperforms the existing ones.