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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.


Related in: MedlinePlus

Subjective evaluation results. (a) Stereoscopic effect; (b) temporal smoothness.
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sensors-15-15246-f019: Subjective evaluation results. (a) Stereoscopic effect; (b) temporal smoothness.

Mentions: Figure 19 shows the values of the two factors acquired by experiments for the four evaluation video sequences. From the experimental results of subjective assessment, we can find that the proposed algorithm has advantages in stereoscopic effect and temporal smoothness. Better stereoscopic effect is due to the adaptability to different image types of the proposed algorithm. Better temporal smoothness can be attributed to the time-coherent depth maps generated in the process. Thus by using the proposed algorithm, viewers can reduce eye fatigue while they are enjoying stereoscopic videos compared to other algorithms.


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)

Subjective evaluation results. (a) Stereoscopic effect; (b) temporal smoothness.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15246-f019: Subjective evaluation results. (a) Stereoscopic effect; (b) temporal smoothness.
Mentions: Figure 19 shows the values of the two factors acquired by experiments for the four evaluation video sequences. From the experimental results of subjective assessment, we can find that the proposed algorithm has advantages in stereoscopic effect and temporal smoothness. Better stereoscopic effect is due to the adaptability to different image types of the proposed algorithm. Better temporal smoothness can be attributed to the time-coherent depth maps generated in the process. Thus by using the proposed algorithm, viewers can reduce eye fatigue while they are enjoying stereoscopic videos compared to other algorithms.

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.


Related in: MedlinePlus