Depth Image Based Rendering (DIBR)

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Survey on Depth Map Pre-processing Techniques in Depth Image Based Rendering

Survey on Depth Map Pre-processing Techniques in Depth Image Based Rendering

ABSTRACT: This paper presents a survey on different depth map pre-processing techniques used in DIBR (depth image based rendering). The recent years have witnessed 3-dimensional technology to become increasingly popular, as it can provide high quality and immensive experience to end users.DIBR is a 2-D to-3-D conversion technology that is used in many applications such as 3DTV and auto stereoscopic displays. One of the main problems in DIBR is how to reduce holes that occurred on the generated virtual view images due to the disoccluded areas in the depth map. For many applications the input depth map are demanded to be complete and precise which is hard to be met by existing depth sensing techniques. The problem can be solved by incorporating depth enhancement methods as a pre-processing step.
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Depth Image Based Rendering Process For 2d To 3d Conversion

Depth Image Based Rendering Process For 2d To 3d Conversion

visual quality as compared to two-dimensional (2D) technology. In present era every multimedia device needs 3D technology. So for generation of 3D content there is need of Depth image based rendering (DIBR) process which will generate left and right image through depth image and original image. Basically DIBR is following the concept of actual 3D recording camera setup. Through original camera setup there is virtual camera formula is generated which will create left and right image. Using both images, 3D content is created. As we already know for any image processing application time complexity is main issue. So in this work we will propose a fast and approximate DIBR algorithm which will reduce the time complexity issue. For image quality measurement there is some scientific parameter we will use which will check the quality of generated left and right image through proposed DIBR algorithm. Those parameters are like PSNR, SSIM, RFSIM and FSIM. Algorithm will implement on Matlab.
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Fast And Approximate Processing Unit For Depth Image Based Rendering Process

Fast And Approximate Processing Unit For Depth Image Based Rendering Process

The 3D video signal processing has received considerable attention in visual processing. Given advances in 3D display technology, humans aspire to experience more realistic and unique 3D effects. Depth-Image-Based-Rendering (DIBR) is a key technology in advanced three dimensional television system (ATTEST 3D TV System) [2][3]. Depth image based rendering contains three steps [4].Pre-processing of depth map is applied for reducing the sharp horizontal transitions on depth map. Second, 3D image warping renders left and right images based on the pre-processed depth map and intermediate color image. On the basis of my Survey Paper the traditional 3D game has some information of the third dimension, but it is still two-dimensional when it is displayed on screen, while the stereoscopic game has stereoscopic view objects in the images stand out of the screen[1]. The depth preprocessing method is applied before transmission. Once both the original image and the accompanying depth image are received, the auto stereoscopic image is produced by applying 3D Wrapping, hole filling, and merging simultaneously[2][3].
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Spatio-temporal consistent depth-image-based rendering using layered depth image and inpainting

Spatio-temporal consistent depth-image-based rendering using layered depth image and inpainting

Depth-image-based rendering (DIBR) is a commonly used method for synthesizing additional views using video-plus-depth (V+D) format. A critical issue with DIBR-based view synthesis is the lack of information behind foreground objects. This lack is manifested as disocclusions, holes, next to the foreground objects in rendered virtual views as a consequence of the virtual camera “seeing” behind the foreground object. The disocclusions are larger in the extrapolation case, i.e. the single camera case. Texture synthesis methods (inpainting methods) aim to fill these disocclusions by producing plausible texture content. However, virtual views inevitably exhibit both spatial and temporal inconsistencies at the filled disocclusion areas, depending on the scene content. In this paper, we propose a layered depth image (LDI) approach that improves the spatio-temporal consistency. In the process of LDI generation, depth information is used to classify the foreground and background in order to form a static scene sprite from a set of neighboring frames. Occlusions in the LDI are then identified and filled using inpainting, such that no disocclusions appear when the LDI data is rendered to a virtual view. In addition to the depth information, optical flow is computed to extract the stationary parts of the scene and to classify the occlusions in the inpainting process. Experimental results demonstrate that spatio-temporal inconsistencies are significantly reduced using the proposed method. Furthermore, subjective and objective qualities are improved compared to state-of-the-art reference methods.
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Depth-image-based rendering with spatial and temporal texture synthesis for 3DTV

Depth-image-based rendering with spatial and temporal texture synthesis for 3DTV

provide comfortable stereo parallax and smooth motion disparity by displaying multiview images of the same scene simultaneously. A simple approach is to capture, com- press, and transmit multiple views directly. The current multiview video coding standard [5,6] with high compres- sion efficiency, which exploits the spatial correlations of the neighboring views, is used to encode and decode the multiple video streams, generally more than eight views. But the transmission bandwidth cost remains a challeng- ing and unresolved problem. Meanwhile, it is commonly suggested that the future 3DTV systems should have com- pletely decoupled capture and display operations [7]. A proper abstract intermediate representation of the cap- tured data, video plus depth format, is proposed by Fehn [8] to achieve such a decoupled operation with an accept- able increment of bandwidth. The depth-image-based rendering (DIBR) [2] algorithm will be used to render multiple perspective views from the video plus depth data according to the requirement of autostereoscopic displays. Thus, the DIBR method has attracted much attention, and become a key technology of the 3DTV system [1].
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Energy Resilient & Power Aware Approximate Processing Unit For Depth Image Based Rendering Process

Energy Resilient & Power Aware Approximate Processing Unit For Depth Image Based Rendering Process

In present ere every human demands number of multimedia application. Those applications are based on graphics and computer vision. Similar in this era every one want realistic view so there is tremendous demand of Three- dimension (3D) technology. As we know 3D technology increases the visual quality as compared to two-dimensional (2D) technology. For generation of 3D content there is need of Depth image based rendering (DIBR) process which will generate left and right image through depth image and original image. Basically DIBR is following the concept of actual 3D recording camera setup. Through original camera setup there is virtual camera formula is generated which will create left and right image. Using both image 3D content is created. As we already know for any image processing application time complexity is main issue. So in this work I will propose a fast and approximate DIBR algorithm which will reduce the time complexity issue. In this work we will represent the hardware and algorithm implementation of our proposed design. Here image quality measurement there is some scientific parameter will use which will check the quality of generated left and right image through proposed DIBR algorithm. Those parameters are like PSNR, SSIM, RFSIM and FSIM. Algorithm will implement on Matlab.
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Abstract: Several depth image based rendering (DIBR) watermarking methods have been proposed,

Abstract: Several depth image based rendering (DIBR) watermarking methods have been proposed,

Abstract: Several depth image based rendering (DIBR) watermarking methods have been proposed, but they have various drawbacks, such as non-blindness, low imperceptibility, and vulnerability to signal or geometric distortion. This paper proposes a template based DIBR watermarking method that overcomes the drawbacks of previous methods. The proposed method exploits two properties to resist DIBR attacks: the pixel is only moved horizontally by DIBR, and the smaller block is not distorted by DIBR. The one dimensional (1D) discrete cosine transform (DCT) and curvelet domains are adopted to utilize these two properties. A template is inserted in the curvelet domain to restore the synchronization error caused by geometric distortion. A watermark is inserted in the 1D DCT domain to insert and detect a message from the DIBR image. Experimental results of the proposed method show high imperceptibility and robustness to various attacks, such as signal and geometric distortions. The proposed method is also robust to DIBR distortion and DIBR configuration adjustment, such as depth image preprocessing and baseline distance adjustment.
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DIBR-synthesized image quality assessment based on morphological multi-scale approach

DIBR-synthesized image quality assessment based on morphological multi-scale approach

Newspaper (1024 × 768, 9 cameras with 5 cm spacing). The selected contents are representative and used by MPEG also. For each sequence four virtual views are gen- erated on the positions corresponding to those positions obtained by the real cameras using seven depth-image- based rendering algorithms, named A1-A7 [45–50]. One key frame from each synthesized sequence is randomly chosen for the database. For these key frames subjective assessment in form of mean opinion scores (MOS) is pro- vided. The difference mean opinion scores (DMOS) is cal- culated as the difference between the reference frame’s MOS and the synthesized frame’s MOS. In the algorithm A1 [45], the depth-image is pre-processed by a low-pass filter. Borders are cropped and then the image is interpo- lated to reach its original size. The algorithm A2 is based on A1 except that the borders are not cropped but inpainted by the method described in [46]. The algorithm A3 [47] use inpainting method [46] to fill in the missing parts in the virtual image which introduces blur in the dis- occluded area. This algorithm was adopted as the refer- ence software for MPEG standardization experiments in 3D Video group. The algorithm A4 performs hole-filling method aided by depth information [48]. The algorithm A5 uses a patch-based texture synthesis as the hole-filling method [49]. The algorithm A6 uses depth temporal infor- mation to improve synthesis in the disoccluded areas [50]. The frames generated by algorithm A7 contain unfilled holes. Due to very noticeable object shifting artifacts in the frames generated by algorithm A1, these frames are excluded from the tests. The focus remains on images synthesized using A2–A7 DIBR algorithms and without registration procedure for alignment of the synthesized and the original frames. The results presented in Sec- tions 4.2–4.4 for the IRCCyN/IVC DIBR database are based on the mixed statistics of the DIBR algorithms A2-A7.
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A Review Paper on 2D to 3D Conversion of HD Medical Images

A Review Paper on 2D to 3D Conversion of HD Medical Images

The results demonstrate our method is effective in showing the tumour location. D. Vetriselvi*, Mr. L. Megalan Leo jan2015 [19]: Thus this paper presents a novel based approach to convert 2D images. While converting 2D to 3D some of the key features have to be considered like (i) parameters like shape, motion, colour, texture, edges, (ii) Depth cues have to be found to generate depth maps, (iii) Fully automatic or semi – automatic method involve decision making by human operators and have been much successful in providing expected results and also time-consuming. Fully-automatic methods almost make strong assumptions about the 3D scene. Thus, in this paper, a detailed survey on the existing methods for 2D to 3D conversion techniques have been discussed to find out the best method that could provide reliable and promising results. AnamikaPatre Ravi Tiwari may 2016[20]: Video, audio and multimedia offer powerful means of communication. Moving pictures are excellent for showing how things change or how something is done, for establishing a context for information (such as a landscape or a working environment) to make it easier for an audience to relate to what you are saying. Three-dimension (3D) technology increases the visual quality as compared to (2D) technology. In present era every multimedia device needs 3D technology. 2D to 3D conversion is basically based on accurate algorithm. This work presents Swift & Novel algorithm for 2D to 3D Conversion of HD Image/video. This approach will automatically convert 2D content into 3D content. In this work a novel approximate algorithm for 2D to 3D conversion. That algorithm include depth map generation unit and depth image based rendering (DIBR) process.
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View invariant DIBR-3D image watermarking using DT-CWT

View invariant DIBR-3D image watermarking using DT-CWT

In recent time, depth-image-based rendering (DIBR) based 3D image representation [8, 36] becomes popular due to its compression efficiency. It has been observed in literature [15, 17, 29] that the efficient watermarking system for authenticating DIBR 3D image encoding should consider situations not only where both the virtual left and right views are illegally distributed as 3D content but also where each single view, including the original center view, illegitimately transmitted [5, 32, 33]. Due to certain inherent features like pixel disparity and changes in the depth image etc., the direct extension of existing conventional watermarking schemes for 2D and stereo images [4, 13, 21, 28, 30, 35] are not very useful for DIBR [9] based encoding. In other words, the main challenge is to embed the watermark in such a way that the watermark should resist the view generation process (it can be treated as a potential attack named as synthesis view attack) of the DIBR technique.
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Swift and Novel Algorithm for 2D to 3D Conversion of HD Image using Energy Reduction Approach

Swift and Novel Algorithm for 2D to 3D Conversion of HD Image using Energy Reduction Approach

Video, audio and multimedia offer powerful means of communication. Moving pictures are excellent for showing how things change or how something is done, for establishing a context for information (such as a landscape or a working environment) to make it easier for an audience to relate to what you are saying. Three-dimension (3D) technology increases the visual quality as compared to (2D) technology. In present era every multimedia device needs 3D technology. The depth information needed for the generation of 3D form 2D content. Therefore, conversion of 2D video/image into 3D has become an important issue in emerging 3Dapplication. 2D to 3D conversion is basically based on accurate algorithm. This work presents Swift & Novel algorithm for 2D to 3D Conversion of HD Image/video. This approach will automatically convert 2D content into 3D content. In this work a novel approximate algorithm for 2D to 3D conversion. That algorithm include depth map generation unit and depth image based rendering (DIBR) process.
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Error resilience techniques for wireless 3-D video transmission

Error resilience techniques for wireless 3-D video transmission

3-D video with depth image based rendering (DIBR). The video-plus-depth for- mat is partitioned into two sequences, i.e., a color sequence and a depth sequence, according to their respective importance to the overall quality of the 3-D video. In this approach, the highly important color sequence is better protected with the most significant bits (MSBs) of 16-QAM, while the less important depth sequence uses the less significant bits (LSBs).

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Detection of the Single Image from DIBR Based on 3D Warping Trace and Edge Matching

Detection of the Single Image from DIBR Based on 3D Warping Trace and Edge Matching

3D depth can be perceived by binocular disparity. To introduce the 3D depth perception for human, 3D contents contains two slightly different images for left and right human eyes. Because 3D contents have two images, dif- ferent formats compared to the formats of traditional 2D image are used to store the 3D contents. 3D contents are usually stored (or produced) in two representative formats. One, which is called stereo image rendering (SIR), is to store left and right images which are simultaneously taken for a scene. This method provides the high quality view because of two fine views for both left and right eyes. However, the recording process for the fine quality requires high costs to set the configurations of two different cameras such as same height, brightness and the tone of color. Furthermore, the depth configuration is fixed and the bandwidth for the transmission is doubled compared to the traditional TV broadcast system.
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A Global Nearest Neighbour Depth Learning based, automatic 2D to 3D image conversion

A Global Nearest Neighbour Depth Learning based, automatic 2D to 3D image conversion

This is one of the most successful approach for 2D-to-3D conversion. This method require a significant operator intervention in the conversion process, fitting delineating objects in individual frames, placing them at suitable depths, and correcting errors after final rendering. These methods are adopted by various companies such as Imax Corp., Digital Domain Productions Inc etc. Many films have been converted to 3D using this approach.

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Digital Refocusing: All in Focus Image Rendering Based on Holoscopic 3D Camera

Digital Refocusing: All in Focus Image Rendering Based on Holoscopic 3D Camera

Introducing the depth information to full resolution method is to sustain natural looking photographic image. Therefore two approaches were presented in [19] to minimize artifacts. When selecting one patch size under each microlens and combining them together will only return one plane of the image. In other word the different patch size acts as refocusing feature, where different size patches look at a different plane. Furthermore, the depth estimating algorithm was applied to find the matching position of the same patch size from its neighboring microlens. This will remedy the artifacts problems in the images. Unfortunately this process is time consuming as it requires matching position of individual element images from its four neighboring element images. In this paper, a new approach is introduced to get full use of the viewpoint images by in-cooperating a new refocusing technique to improve the visual resolution.
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Real-Time Image Based Rendering for Stereo Views of Vegetation

Real-Time Image Based Rendering for Stereo Views of Vegetation

Chen and Williams 4 carried the texturing concept further by using interpolated images to portray three-dimensional scenes. Their method exploits the fact that a sequence of images from closely spaced viewpoints would be highly coherent. Each image stores a set of camera parameters and a depth buffer. Using each pixel’s screen coordinates (x, y and z) and the camera’s relative location, a correspondence between the pixels in each pair of images is established by a 4x4 transformation matrix. The transformations are reduced to 3D spatial offset vectors for each of the pixels. The offset vector indicates the amount each of the pixels moves in its screen space as a result of the camera’s movement. The offset vectors are pre-computed and stored in a morph map, which represents forward mapping from one image to another. Using these maps, smooth interpolation between views is used to obtain the image from the current viewing position. These maps are better suited for smooth textured surfaces but not apt for objects like vegetation, which has high depth complexity.
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Occlusion-based Direct Volume Rendering for Computed Tomography Image

Occlusion-based Direct Volume Rendering for Computed Tomography Image

Direct volume rendering (DVR) is an important 3D visualization method for medical images as it depicts the full volumetric data. However, because DVR renders the whole volume, regions of interests (ROIs) such as a tumor that are embedded within the volume maybe occluded from view. Thus, conventional 2D cross-sectional views are still widely used, while the advantages of the DVR are often neglected. In this study, we propose a new visualization algorithm where we augment the 2D slice of interest (SOI) from an image volume with volumetric information derived from the DVR of the same volume. Our occlusion-based DVR augmentation for SOI (ODAS) uses the occlusion information derived from the voxels in front of the SOI to calculate a depth parameter that controls the amount of DVR visibility which is used to provide 3D spatial cues while not impairing the visibility of the SOI. We outline the capabilities of our ODAS and through a variety of computer tomography (CT) medical image examples, compare it to a conventional fusion of the SOI and the clipped DVR.
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Bayesian surface estimation from multiple cameras using a prior based on the visual hull and its application to image based rendering

Bayesian surface estimation from multiple cameras using a prior based on the visual hull and its application to image based rendering

First, a multiresolution segmentation algorithm is applied to the images captured from the scene to separate figure and background. From the silhouettes derived from the seg- mentation, a view-dependent visual hull is constructed following a similar method to that described in [11]. From the visual hull the depth and surface orientation along any ray in the scene may be derived using total least squares, a robust method of fitting planes to surface data [12]. These estimates are used as a prior in a multiscale particle filter, which provides the final surface estimates, in terms of a number of disjoint quadrilaterals, cor- responding to square blocks in each image of the scene. The patches can then be tracked over time using a second particle filter [14], and used to reconstruct arbitrary views of the scene using an adaptation of a conventional graphics renderer, giving real-time recon- structions [13], [17].
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OSG Based Real time Volume Rendering Algorithm for Electromagnetic Environment

OSG Based Real time Volume Rendering Algorithm for Electromagnetic Environment

The OSG 3D rendering engine consists by a series of graphics-related functional modules, it provides scene management and graphics rendering optimizations primarily for the development of graphical image applications [20]. Because of its cross-platform nature, it can be run on most types of operating systems, which allows the OSG developers to develop on their favorite platforms and configure them based on the platform users’ demand. Compared with the industry standard OpenGL, OSG encapsulates and provides a multitude of algorithms to enhance the program performance in addition to open-source and cross-platform features. Therefore, this paper uses the OSG and GLSL shading language to achieve the hardware-accelerated ray casting algorithm, the algorithm implementation process is shown in Figure 5.
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3D rendering method for MRI image: a survey

3D rendering method for MRI image: a survey

the overall processing time of a raycasting algorithm. The most simple memory layout for raycasting is a three- dimensional array. However, this may lead to view- dependent render times, due to changing memory access patterns for different viewing directions. This can greatly affect the performance for large datasets. Another common storage scheme is bricking (Parker et al. 1999), where the volume data is stored as sub-cubes of a fixed size. In general, this approach reduces the view dependent performance variations but does not increase the memory consumption. Law and Yagel have developed a thrashless raycasting method based on such a memory layout (Law and Yagel 1996). In their approach, all resampled locations within one block are processed before the algorithm continues to process the next block. Knittel (2000) and Mora et al. (2002) achieved impressive performance by using a spread memory layout. The main drawback of such an approach is the enormous memory usage. In both systems, the memory usage is approximately four times the data size.
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