54.2: Natural Stereo Depth Creation Methodology for a Real‐time 2D‐to‐3D Image Conversion

Iinuma, T; Murata, Haruhiko; Yamashita, S.; Oyamada, K.

doi:10.1889/1.1832883

Cited by 10 publications

(4 citation statements)

References 2 publications

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“…However, this method fails when the objects have vertical motion. A hybrid integration of CID and MTD called DCP 16 is the first literature considering both the monocular cue and binocular cue. Although the depth from motion can generate an effective depth map, the method fails to generate a depth map when the camera does not have motion.…”

Section: Study On Previous Workmentioning

confidence: 99%

Video 2‐D—to—3‐D conversion based on hybrid depth cueing

Cheng

Chen

2010

J Soc Info Display

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With the maturation of three-dimensional (3-D) technologies, display systems can provide higher visual quality to enrich the viewer experience. However, the depth information required for 3-D displays is not available in conventional 2-D recorded contents. Therefore, the conversion of existing 2-D video to 3-D video becomes an important issue for emerging 3-D applications. This paper presents a system which automatically converts 2-D videos to 3-D format. The proposed system combines three major depth cues: the depth from motion, the scene depth from geometrical perspective, and the fine-granularity depth from the relative position. The proposed system uses a block-based method incorporating a joint bilateral filter to efficiently generate visually comfortable depth maps and to diminish the blocky artifacts. By means of the generated depth map, 2-D videos can be readily converted into 3-D format. Moreover, for conventional 2-D displays, a 2-D image/video depth perception enhancement application is also presented. With the depth-aware adjustment of color saturation, contrast, and edge, the stereo effect of the 2-D content can be enhanced. A user study on subjective quality shows that the proposed method has promising results on depth quality and visual comfort.

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Section: Study On Previous Workmentioning

confidence: 99%

Video 2‐D—to—3‐D conversion based on hybrid depth cueing

Cheng

Chen

2010

J Soc Info Display

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“…Notable among Single-frame Methods is the attempt by Battiato [4] who made use of multiple cues, a progression over some other methods that concentrated on single cuesfocal proximity [5], machine learning algorithms [6][7], and 2 D proportional dimensions (height and width ratio) and texture changes [8] and geometric measurements. A significant development in Multi-frame Method is the successful hybridization of MTD (Modified Time Difference) and CID (Computed Image Depth) achieved by Iinuma et al, [9] that uses triangulation successfully to generate close to real 3D images. Recent approaches [18]- [20] that showed commendable results when utilized in a different application but can also be deployed to solve for 2D to 3D rendering.…”

Section: Introductionmentioning

confidence: 99%

Addressing Latency Issues in 2D to 3D Conversion: Deploying Available Synthetic Database

Manasa

2018

IJ-ICT

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Conventional 2D to 3D rendering techniques involve a sequential process of grouping of the input images based on edge information and predictive algorithms to assign depth values to pixels with same hue. The iterative calculations and volume of data under scrutiny to assign ‘real-time’ values raise latency issues and cost considerations. For commercial consumption, where speed and accuracy define the viability of a product, there is a need to reorient the approach used in the present methodologies. In predictive methodologies one of the core interests is achieving the initial approximation as close to the ‘real’ value as possible. In this work, ‘synthetic’ database is used to provide the first approximation through comparison techniques and fed to the predictive tool. It is believed that this work will provide a basis for developing an efficient 2D to 3D conversion methodology.

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“…The major depth perceptions are binocular cue from two eyes and various monocular cues from single eye. [16]. Therefore, in order to create high quality depth map from single view video, both binocular and monocular cues need to be explored.…”

Section: Introductionmentioning

confidence: 99%

“…When watching real world, human brain integrates various heuristic depth cues to generate depth perception. [16]. Therefore, in order to create high quality depth map from single view video, both binocular and monocular cues need to be explored.…”

Section: Introductionmentioning

confidence: 99%

11.4: A Quality‐Scalable Depth‐Aware Video Processing System

Cheng

Tsai

et al. 2009

Symp Digest of Tech Papers

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The three-dimensional (3D) Introduction3D video signal processing has become a trend in the visual processing field. As 3D display technology matures, people aspire to experience the videos that closer to reality. Emerging 3D displays generate better stereo effects than conventional 2D displays. However, for existing 2D content, depth information is not recorded. The lack of an effective 3D content generation approach is a dilemma for 3D industry. Therefore, depth-aware video processing becomes an important issue.The main concept of depth generation is to retrieve the third dimensional information from multiple depth cues of existing 2D video content. When watching real world, human brain integrates various heuristic depth cues to generate depth perception. . Therefore, in order to create high quality depth map from single view video, both binocular and monocular cues need to be explored. In this paper, we propose a depth-aware video processing system. The proposed system can provides depth map and multi-view video for 3D/stereoscopic displays and also can enhance the depth perception on conventional 2D displays. Proposed Video Processing SystemThe system we proposed has three major cores, depth generation, depth-aware 2D video enhancement, and multi-view depth imagebased rendering (DIBR) [7], as shown in Figure 1. The detail of each part is explained in the following sub-sections. Depth GenerationThe depth generation module is the major part in the system. The module combines multiple depth cues. They are depth from motion parallax (DMP), depth from geometrical perspective (DGP), and depth from relative position (DRP). These cues are integrated by priority depth fusion method as shown in Figure 2.In binocular depth cue, multiple frames in temporal domain are used to approximate two views in spatial domain [5]. Firstly, the camera motion of the consecutive frames with respect to the current frame is analyzed by global motion estimation. The frame with most suitable baseline is selected and is warped to form a parallel view configuration with the current frame to compensate the camera motion.Therefore, the warped frame and the current frame can be approximated as binocular images. The disparity in spatial domain can be seen as motion parallax in temporal domain. Motion parallax is computed by block-based matching with spatial smoothness consideration. The spatial smoothness is applied to generate smooth motion vector using local optimization method.

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54.2: Natural Stereo Depth Creation Methodology for a Real‐time 2D‐to‐3D Image Conversion

Cited by 10 publications

References 2 publications

Video 2‐D—to—3‐D conversion based on hybrid depth cueing

Video 2‐D—to—3‐D conversion based on hybrid depth cueing

Addressing Latency Issues in 2D to 3D Conversion: Deploying Available Synthetic Database

11.4: A Quality‐Scalable Depth‐Aware Video Processing System

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