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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 the depth map generation method.
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sensors-15-15246-f002: Flowchart of the depth map generation method.

Mentions: An input 2D video stream consists of a number of video shots and each video shot includes a sequence of frames taken using a single camera. As the camera moves slightly within a video shot, the global depth gradient is changed slightly and it can be shared by frames in the same video shot to reduce the computational complexity and promote the temporal smoothness of the depth maps with little extra inaccuracy, which is proved by our experimental results in Section 3. Whether the input frame of video stream belongs to a new video shot is first detected. If the input frame is a new video shot frame, image type of this frame is judged and the global depth gradient is assigned accordingly. If the input frame is not a new video shot frame, the global depth gradient of the previous frame is directly adopted. That is, the global depth gradient is calculated from the first frame of a video shot and reused in other frames within the same video shot. The local depth information of the input frame is then utilized to generate the depth map. Figure 2 illustrates the flowchart of the depth map generation method.


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 the depth map generation method.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15246-f002: Flowchart of the depth map generation method.
Mentions: An input 2D video stream consists of a number of video shots and each video shot includes a sequence of frames taken using a single camera. As the camera moves slightly within a video shot, the global depth gradient is changed slightly and it can be shared by frames in the same video shot to reduce the computational complexity and promote the temporal smoothness of the depth maps with little extra inaccuracy, which is proved by our experimental results in Section 3. Whether the input frame of video stream belongs to a new video shot is first detected. If the input frame is a new video shot frame, image type of this frame is judged and the global depth gradient is assigned accordingly. If the input frame is not a new video shot frame, the global depth gradient of the previous frame is directly adopted. That is, the global depth gradient is calculated from the first frame of a video shot and reused in other frames within the same video shot. The local depth information of the input frame is then utilized to generate the depth map. Figure 2 illustrates the flowchart of the depth map generation method.

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.