Robust Stereoscopic Crosstalk Prediction

Abstract

We propose a new metric to predict perceived crosstalk using the original images rather than both the original and ghosted images. The proposed metrics are based on color information. First, we extract a disparity map, a color difference map, and a color contrast map from original image pairs. Then, we use those maps to construct two new metrics ( $V_{\mathrm{ dispc}}$ and $V_{\mathrm{ dlogc}}$ ). Metric $V_{\mathrm{ dispc}}$ considers the effect of the disparity map and the color difference map, while $V_{\mathrm{ dlogc}}$ addresses the influence of the color contrast map. The prediction performance is evaluated using various types of stereoscopic crosstalk images. By incorporating $V_{\mathrm{ dispc}}$ and $V_{\mathrm{ dlogc}}$ , the new metric $V_{\mathrm{ pdlc}}$ is proposed to achieve a higher correlation with the perceived subject crosstalk scores. Experimental results show that the new metrics achieve better performance than previous methods, which indicate that color information is one key factor for crosstalk visible prediction. Furthermore, we construct a new data set to evaluate our new metrics.