Motion-based depth estimation for 2D-to-3D video conversion

Wang, Ming-Jiun; Chen, Chun-Fu; Lee, Gwo Giun

doi:10.1109/vcip.2013.6706329

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Figure 2 shows block diagram of camera motion scenario detection. We build this procedure on the work of Wang et al [9], and five camera motion scenarios are shows in TABLE I. The co-occurrence matrix is established by:…”

Section: Content-adaptive Depth Map Enhancementmentioning

confidence: 99%

Content-adaptive depth map enhancement based on motion distribution

Lee

Chen

2014

2014 IEEE Visual Communications and Image Processing Conference

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This paper provides a motion-based contentadaptive depth map enhancement algorithm to enhance the quality of the depth map and reduce the artifacts in the synthesized views. The proposed algorithm extracts depth cues from the motion distribution at the specific scenario of camera movement to align the distribution of depth and motion. In real world scenarios, when the camera is panning in horizontal direction, the nearer distance between the object and the camera, the larger motion will be, and vice versa; therefore, we could interpret the depth from motion in this. Moreover, in the scenario of fixed camera, the depth cue from motion could be derived in the same approach, and the depth variation within one moving object shall be small. Hence, the depth values of moving object should not be rapidly changing. In addition, this paper also employs the bi-directional motion-compensated infinite impulse response low-pass filter to stabilize the consistency of depth maps over time. As a consequence, the algorithm so introduced not only aligns the depth map to depth cues from motion but also enhance stability and consistency of depth maps in the spatial-temporal domain. Experiment results via enhanced depth maps show that the synthesized results would be better in both objective and subjective measurement in comparison with the results using original depth maps and the state-of-the-art depth enhancement algorithms.

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Section: Content-adaptive Depth Map Enhancementmentioning

confidence: 99%

Content-adaptive depth map enhancement based on motion distribution

Lee

Chen

2014

2014 IEEE Visual Communications and Image Processing Conference

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Motion-Based Depth Estimation for 2D to 3D Video Conversion

Guo

Tang²,

Zou

2016

J. Adv. Comput. Intell. Intell. Inform.

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Recent advances in 3D have increased the importance of stereoscopic content creation and processing. Therefore, converting existing 2D videos into 3D videos is very important for growing 3D market. The most difficult task in 2D-to-3D video conversion is estimating depth map from single-view frame images. Thus, in this paper, we propose a novel motion-based 2D to 3D video conversion method. The method first determines the motion type using the optical flow estimation. Then, different depth estimation processes are performed based on the motion type. For global motion, the depth from motion parallax provides the final depth map. For local motion, the depth from template together with the bilateral filter is used to produce the depth map. Finally, the left- and right-view images are synthesized to generate realistic stereoscopic results for viewers. During the process, the visual artifacts of the synthesized virtual views are effectively eliminated by recovering the separation and loss of foreground objects. A comparative study and quantitative evaluation with other conversion methods are carried out, which demonstrate that better overall quality results may be obtained using the proposed method.

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Incorporation of Depth in Two Dimensional Video Captures

Barthakur

Sarma

2016

Emerging Technologies in Intelligent Applications for Image and Video Processing

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Stereoscopic vision in cameras is an interesting field of study. This type of vision is important in incorporation of depth in video images which is needed for the ability to measure distances of the object from the camera properly i.e. conversion of two dimensional video image into three dimensional video. In this chapter, some of the basic theoretical aspects of the methods for estimating depth in 2D video and the current state of research have been discussed. These methods are frequently used in the algorithms for estimating depth in the 2D to 3D video techniques. Some of the recent algorithms for incorporation depth in 2D video are also discussed and from the literature review a simple and generic system for incorporation depth in 2D video is presented.

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Motion-based depth estimation for 2D-to-3D video conversion

Cited by 3 publications

References 15 publications

Content-adaptive depth map enhancement based on motion distribution

Content-adaptive depth map enhancement based on motion distribution

Motion-Based Depth Estimation for 2D to 3D Video Conversion

Incorporation of Depth in Two Dimensional Video Captures

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