Stereoscopic three-dimensional (3D) video services

which aim to provide realistic and immersive experiences

have gained considerable acceptance and interest. Visual saliency detection can automatically predict

locate

and identify important visual information

as well as help machines to effectively filter valuable information from high-volume multimedia data. Saliency detection models are widely studied for static or dynamic 2D scenes. However

the saliency problem of stereoscopic 3D videos has received less attention. Moreover

few studies are related to dynamic 3D scenes. Given that 3D characteristics

such as depth and visual fatigue

affect the visual attention of humans

the saliency models of static or dynamic 2D scenes are not directly applicable for 3D scenes. To address the gap in the literature

we propose a novel model for 3D salient region detection in stereoscopic videos. The model utilizes multi-dimensional

perceptual

and binocular characteristics. The proposed model computes the visual salient region for stereoscopic videos from spatial

depth

and temporal domains of stereoscopic videos. The proposed algorithm is partitioned into four blocks:the measures of spatial

depth

temporal (motion) saliency

and fusion of the three conspicuity maps. In the spatial saliency module

the algorithm considers the spatial saliency in each frame of videos as a visual attention dimension. The Bayesian probabilistic framework is adopted to calculate the 2D static conspicuity map. The spatial saliency in the framework emerges naturally as self-information of visual features. These visual features are obtained from the spatial natural statistics of each stereoscopic 3D video frame rather than from a single test frame. In the depth saliency module

the algorithm considers depth as an additional visual attention dimension. Depth signals have specific characteristics that differ from those of natural signals. Therefore

the measure of depth saliency is derived from depth-perception characteristics. The model extracts the foreground saliency from a disparity map

which is combined with depth contrast to generate a depth conspicuity map. In the motion (temporal) saliency module

the algorithm considers motion as another visual dimension. The optical flow algorithm is applied to acquire the inter-frame motion information between adjacent frames. To reduce the computational complexity of optical flow algorithms

the model first extracts the salient region of the current frame in accordance with the previously obtained spatial conspicuity map and depth conspicuity map. The Lucas-Kanade optical flow algorithm is adopted to calculate the motion characteristics between local salient regions of adjacent frames

and the motion conspicuity map is produced by the regional motion vector map. In the fusion step

a new pooling approach is developed to combine the three conspicuity maps to obtain the final saliency map for stereoscopic 3D videos. This fusion approach is based on the principle that human visual systems simultaneously focus on a unique salient region and divert attention to several salient regions in a saliency map. To generate the final saliency maps of stereoscopic videos

the proposed approach replaces the conventional average weighted sum for the fusion of different features and uses a fusion method that is based on global-local difference. We evaluated the proposed scheme for stereoscopic video sequences with various scenarios. Moreover

we compared the proposed model with five other state-of-the-art saliency detection models. The experimental results indicated that the proposed model is efficient

effective

and has superior precision and recall with an 80% precision and 72% recall rate. The proposed model demonstrated its efficiency and effectiveness in saliency detection for stereoscopic videos. The model can be applied to stereoscopic videos or image coding

stereoscopic videos or image quality assessment

and object detection and recognition.