Channel-Mismatch Detection Algorithm for Stereoscopic Video Using Convolutional Neural Network

Авторы: Sergey Lavrushkin, Dmitriy Vatolin
Сборник: Proceedings of the 2018 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON)
Год издания: 2018
Первая страница: 1
Последняя страница: 4
DOI: 10.1109/3DTV.2018.8478542
Аннотация: 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.
Добавил в систему: Лаврушкин Сергей Валерьевич

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