CHANNEL-MISMATCH DETECTION ALGORITHM FOR STEREOSCOPIC VIDEO USING CONVOLUTIONAL NEURAL NETWORK

Abstract

Channel mismatch (the result of swapping left and right views) is a 3D-video artifact that can cause major viewer discomfort. This work presents a novel high-accuracy method of channel-mismatch detection. In addition to the features described in our previous work, we introduce a new feature based on a convolutional neural network; it predicts channel-mismatch probability on the basis of the stereoscopic views and corresponding disparity maps. A logistic-regression model trained on the described features makes the final prediction. We tested this model on a set of 900 stereoscopic-video scenes, and it outperformed existing channel-mismatch detection methods that previously served in analyses of full-length stereoscopic movies.