Stereo Pair Generation by Introducing Bokeh Effect in 2D Images
26 Feb 2015-pp 224-229
TL;DR: Gradient Magnitude Similarity Deviation which is a highly efficient perceptual quality index is used for the performance evaluation of final depth map and GMSD score is lowest in case offinal depth map which shows highest quality.
Abstract: The process of 2D to 3D involves depth map estimation of input image and stereo pair generation using that depth image. Fully automatic techniques are not able to give very high quality output and methods which involve human operators are costly and time consuming. We propose a semiautomatic technique which incorporates monocular depth cues if these are not present in the input image. We propose a method which first includes defocusing factor in the input image semi-automatically and then estimates depth from focus cue. We also used motion parallax as another monocular depth cue and evaluate the performance of final depth map. Gradient Magnitude Similarity Deviation which is a highly efficient perceptual quality index is used for the performance evaluation of final depth map. GMSD score is lowest in case of final depth map which shows highest quality. Finally, stereo pair is generated using original image and its depth map.
References
More filters
13 Jun 2010
TL;DR: It is discovered that “classical” flow formulations perform surprisingly well when combined with modern optimization and implementation techniques, and while median filtering of intermediate flow fields during optimization is a key to recent performance gains, it leads to higher energy solutions.
Abstract: The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that “classical” flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. Moreover, we find that while median filtering of intermediate flow fields during optimization is a key to recent performance gains, it leads to higher energy solutions. To understand the principles behind this phenomenon, we derive a new objective that formalizes the median filtering heuristic. This objective includes a nonlocal term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this new term to include information about flow and image boundaries we develop a method that ranks at the top of the Middlebury benchmark.
1,529 citations
"Stereo Pair Generation by Introduci..." refers methods in this paper
...our experiments, optical flow is used for depth extraction and it is calculated between two consecutive frames taken from the scene as described in [5]....
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TL;DR: It is found that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted images combined with a novel pooling strategy-the standard deviation of the GMS map-can predict accurately perceptual image quality.
Abstract: It is an important task to faithfully evaluate the perceptual quality of output images in many applications, such as image compression, image restoration, and multimedia streaming. A good image quality assessment (IQA) model should not only deliver high quality prediction accuracy, but also be computationally efficient. The efficiency of IQA metrics is becoming particularly important due to the increasing proliferation of high-volume visual data in high-speed networks. We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD). The image gradients are sensitive to image distortions, while different local structures in a distorted image suffer different degrees of degradations. This motivates us to explore the use of global variation of gradient based local quality map for overall image quality prediction. We find that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted images combined with a novel pooling strategy-the standard deviation of the GMS map-can predict accurately perceptual image quality. The resulting GMSD algorithm is much faster than most state-of-the-art IQA methods, and delivers highly competitive prediction accuracy. MATLAB source code of GMSD can be downloaded at http://www4.comp.polyu.edu.hk/~cslzhang/IQA/GMSD/GMSD.htm.
1,211 citations
"Stereo Pair Generation by Introduci..." refers methods in this paper
...Gradient Magnitude Similarity Deviation as described in [15] is used as IQA i....
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Posted Content•
TL;DR: In this article, a gradient magnitude similarity deviation (GMSD) method was proposed for image quality assessment, where the pixel-wise GMS between the reference and distorted images was combined with a novel pooling strategy to predict accurately perceptual image quality.
Abstract: It is an important task to faithfully evaluate the perceptual quality of output images in many applications such as image compression, image restoration and multimedia streaming. A good image quality assessment (IQA) model should not only deliver high quality prediction accuracy but also be computationally efficient. The efficiency of IQA metrics is becoming particularly important due to the increasing proliferation of high-volume visual data in high-speed networks. We present a new effective and efficient IQA model, called gradient magnitude similarity deviation (GMSD). The image gradients are sensitive to image distortions, while different local structures in a distorted image suffer different degrees of degradations. This motivates us to explore the use of global variation of gradient based local quality map for overall image quality prediction. We find that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted images combined with a novel pooling strategy the standard deviation of the GMS map can predict accurately perceptual image quality. The resulting GMSD algorithm is much faster than most state-of-the-art IQA methods, and delivers highly competitive prediction accuracy.
742 citations
01 Jul 2005
TL;DR: This paper presents a fully automatic method for creating a 3D model from a single photograph made up of several texture-mapped planar billboards and has the complexity of a typical children's pop-up book illustration.
Abstract: This paper presents a fully automatic method for creating a 3D model from a single photograph. The model is made up of several texture-mapped planar billboards and has the complexity of a typical children's pop-up book illustration. Our main insight is that instead of attempting to recover precise geometry, we statistically model geometric classes defined by their orientations in the scene. Our algorithm labels regions of the input image into coarse categories: "ground", "sky", and "vertical". These labels are then used to "cut and fold" the image into a pop-up model using a set of simple assumptions. Because of the inherent ambiguity of the problem and the statistical nature of the approach, the algorithm is not expected to work on every image. However. it performs surprisingly well for a wide range of scenes taken from a typical person's photo album.
730 citations
TL;DR: This paper presents a simple yet effective approach to estimate the amount of spatially varying defocus blur at edge locations, and demonstrates the effectiveness of this method in providing a reliable estimation of the defocus map.
Abstract: In this paper, we address the challenging problem of recovering the defocus map from a single image. We present a simple yet effective approach to estimate the amount of spatially varying defocus blur at edge locations. The input defocused image is re-blurred using a Gaussian kernel and the defocus blur amount can be obtained from the ratio between the gradients of input and re-blurred images. By propagating the blur amount at edge locations to the entire image, a full defocus map can be obtained. Experimental results on synthetic and real images demonstrate the effectiveness of our method in providing a reliable estimation of the defocus map.
370 citations
"Stereo Pair Generation by Introduci..." refers methods in this paper
...The method used here recovers depth from a single defocused image captured by uncalibrated conventional camera as described in [18]....
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...And then depth estimation method in [18] is applied as follows....
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