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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.


Comparison between the reused global depth gradient and the calculated global depth gradients in frames with a video shot.
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sensors-15-15246-f014: Comparison between the reused global depth gradient and the calculated global depth gradients in frames with a video shot.

Mentions: The reuse of global depth gradient is another innovation point in the proposed algorithm. As the camera moves slightly within a video shot, the image type of these frames within a video shot does not change and the global depth gradient is changed only slightly during the generation process. As a result, there is no need to calculate the global depth gradient in every frame. Reuse of the global depth gradient within the same video shot brings the benefits of saving time and promoting temporal smoothness with little added inaccuracy. In the experiment, we calculated global depth gradient for every frame in a video shot and then compared them with the reused global depth gradient calculated in the first frame of this video shot, as is shown in Figure 14. The number in Figure 14 represents the frame number in the video shot.


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)

Comparison between the reused global depth gradient and the calculated global depth gradients in frames with a video shot.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15246-f014: Comparison between the reused global depth gradient and the calculated global depth gradients in frames with a video shot.
Mentions: The reuse of global depth gradient is another innovation point in the proposed algorithm. As the camera moves slightly within a video shot, the image type of these frames within a video shot does not change and the global depth gradient is changed only slightly during the generation process. As a result, there is no need to calculate the global depth gradient in every frame. Reuse of the global depth gradient within the same video shot brings the benefits of saving time and promoting temporal smoothness with little added inaccuracy. In the experiment, we calculated global depth gradient for every frame in a video shot and then compared them with the reused global depth gradient calculated in the first frame of this video shot, as is shown in Figure 14. The number in Figure 14 represents the frame number in the video shot.

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.