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A Novel 2D-to-3D Video Conversion Method Using Time-Coherent Depth Maps.

Yin S, Dong H, Jiang G, Liu L, Wei S - Sensors (Basel) (2015)

Bottom Line: In this paper, we propose a novel 2D-to-3D video conversion method for 3D entertainment applications. 3D entertainment is getting more and more popular and can be found in many contexts, such as TV and home gaming equipment. 3D image sensors are a new method to produce stereoscopic video content conveniently and at a low cost, and can thus meet the urgent demand for 3D videos in the 3D entertaiment market.Global depth gradient is computed according to image type while local depth refinement is related to color information.The experimental results prove that this novel method can adapt to different image types, reduce computational complexity and improve the temporal smoothness of generated 3D video.

View Article: PubMed Central - PubMed

Affiliation: Institute of Microelectronics, Tsinghua University, Beijing 100084, China. yinsy@tsinghua.edu.cn.

ABSTRACT
In this paper, we propose a novel 2D-to-3D video conversion method for 3D entertainment applications. 3D entertainment is getting more and more popular and can be found in many contexts, such as TV and home gaming equipment. 3D image sensors are a new method to produce stereoscopic video content conveniently and at a low cost, and can thus meet the urgent demand for 3D videos in the 3D entertaiment market. Generally, 2D image sensor and 2D-to-3D conversion chip can compose a 3D image sensor. Our study presents a novel 2D-to-3D video conversion algorithm which can be adopted in a 3D image sensor. In our algorithm, a depth map is generated by combining global depth gradient and local depth refinement for each frame of 2D video input. Global depth gradient is computed according to image type while local depth refinement is related to color information. As input 2D video content consists of a number of video shots, the proposed algorithm reuses the global depth gradient of frames within the same video shot to generate time-coherent depth maps. The experimental results prove that this novel method can adapt to different image types, reduce computational complexity and improve the temporal smoothness of generated 3D video.

No MeSH data available.


Flowchart of image type judgement.
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sensors-15-15246-f005: Flowchart of image type judgement.

Mentions: If the frame does not belong to the landscape type, the algorithm then judges if the image falls into the linear perspective type by the following procedure: a Hough transform is used to detect straight lines and a threshold is set to obtain main lines from the straight lines. If there are main lines, the intersection points of every two lines are computed. The algorithm also checks if these intersection points are within a predefined range. If so, the input frame can be classified as linear perspective type. The intersection point nearest to the central point of these points can be regarded as the vanishing point and the main lines converging to the vanishing point are regarded as vanishing lines. If the main lines are not detected or the intersection points are decentralized, the frame is classified as normal type. Figure 5 shows the above process.


A Novel 2D-to-3D Video Conversion Method Using Time-Coherent Depth Maps.

Yin S, Dong H, Jiang G, Liu L, Wei S - Sensors (Basel) (2015)

Flowchart of image type judgement.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541829&req=5

sensors-15-15246-f005: Flowchart of image type judgement.
Mentions: If the frame does not belong to the landscape type, the algorithm then judges if the image falls into the linear perspective type by the following procedure: a Hough transform is used to detect straight lines and a threshold is set to obtain main lines from the straight lines. If there are main lines, the intersection points of every two lines are computed. The algorithm also checks if these intersection points are within a predefined range. If so, the input frame can be classified as linear perspective type. The intersection point nearest to the central point of these points can be regarded as the vanishing point and the main lines converging to the vanishing point are regarded as vanishing lines. If the main lines are not detected or the intersection points are decentralized, the frame is classified as normal type. Figure 5 shows the above process.

Bottom Line: In this paper, we propose a novel 2D-to-3D video conversion method for 3D entertainment applications. 3D entertainment is getting more and more popular and can be found in many contexts, such as TV and home gaming equipment. 3D image sensors are a new method to produce stereoscopic video content conveniently and at a low cost, and can thus meet the urgent demand for 3D videos in the 3D entertaiment market.Global depth gradient is computed according to image type while local depth refinement is related to color information.The experimental results prove that this novel method can adapt to different image types, reduce computational complexity and improve the temporal smoothness of generated 3D video.

View Article: PubMed Central - PubMed

Affiliation: Institute of Microelectronics, Tsinghua University, Beijing 100084, China. yinsy@tsinghua.edu.cn.

ABSTRACT
In this paper, we propose a novel 2D-to-3D video conversion method for 3D entertainment applications. 3D entertainment is getting more and more popular and can be found in many contexts, such as TV and home gaming equipment. 3D image sensors are a new method to produce stereoscopic video content conveniently and at a low cost, and can thus meet the urgent demand for 3D videos in the 3D entertaiment market. Generally, 2D image sensor and 2D-to-3D conversion chip can compose a 3D image sensor. Our study presents a novel 2D-to-3D video conversion algorithm which can be adopted in a 3D image sensor. In our algorithm, a depth map is generated by combining global depth gradient and local depth refinement for each frame of 2D video input. Global depth gradient is computed according to image type while local depth refinement is related to color information. As input 2D video content consists of a number of video shots, the proposed algorithm reuses the global depth gradient of frames within the same video shot to generate time-coherent depth maps. The experimental results prove that this novel method can adapt to different image types, reduce computational complexity and improve the temporal smoothness of generated 3D video.

No MeSH data available.