Depth Scaling of Multiview Images for Automultiscopic 3D Monitors

Kim, Manbae; Lee, Seno; Choi, Changyeol; Um, Gi-Mun; Hur, Namho; Kim, Jinwoong

doi:10.1109/3dtv.2008.4547838

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…To avoid this, depth is retargeted to a narrower depth budget, which essentially requires a subsequent warping of the light field according to the new depth that can be done using techniques targeted to stereo content [262], [263]. Depth retargeting techniques are extensive and not the main focus of this survey; we thus only gloss over them briefly: early works explore simple depth remapping functions [264], [265], while others rely on perceptual considerations [266], [267], [268], or devise a means of cutting through the EPI volume generating views with multiple centers of projection [269]. A joint image and depth retargeting based on seam carving for light fields has also been proposed [270].…”

Section: Aliasingmentioning

confidence: 99%

Light Field Image Processing: An Overview

Masiá

Jarabo

et al. 2017

IEEE J. Sel. Top. Signal Process.

492

215

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Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher-dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics and signal processing communities. In this article, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data. Index Terms-Light field imaging, light field processing.

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Section: Aliasingmentioning

confidence: 99%

Light Field Image Processing: An Overview

Masiá

Jarabo

et al. 2017

IEEE J. Sel. Top. Signal Process.

492

215

View full text Add to dashboard Cite

show abstract

“…The conversion relies on image warping and requires manual interaction. Kim et al [2008] discuss how to perform non-linear depth remapping for multi-view autostereoscopic displays. Zwicker et al [2006] present a remapping and a prefiltering framework for automultiscopic displays that adapts an input light field to the display capabilities.…”

Section: Related Workmentioning

confidence: 99%

Multi-perspective stereoscopy from light fields

et al. 2011

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Figure 1: We propose a framework for flexible stereoscopic disparity manipulation and content post-production. Our method computes multi-perspective stereoscopic output images from a 3D light field that satisfy arbitrary prescribed disparity constraints. We achieve this by computing piecewise continuous cuts (shown in red) through the light field that enable per-pixel disparity control. In this particular example we employed gradient domain processing to emphasize the depth of the airplane while suppressing disparities in the rest of the scene. AbstractThis paper addresses stereoscopic view generation from a light field. We present a framework that allows for the generation of stereoscopic image pairs with per-pixel control over disparity, based on multi-perspective imaging from light fields. The proposed framework is novel and useful for stereoscopic image processing and post-production. The stereoscopic images are computed as piecewise continuous cuts through a light field, minimizing an energy reflecting prescribed parameters such as depth budget, maximum disparity gradient, desired stereoscopic baseline, and so on. As demonstrated in our results, this technique can be used for efficient and flexible stereoscopic post-processing, such as reducing excessive disparity while preserving perceived depth, or retargeting of already captured scenes to various view settings. Moreover, we generalize our method to multiple cuts, which is highly useful for content creation in the context of multi-view autostereoscopic displays. We present several results on computer-generated content as well as live-action content.

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“…The phenomenon is explained by high frequency components of such details being aliased due to a small angular sampling rate (few perspective views), and goes under the name inter-perspective aliasing 2 . Two remedies have earlier been proposed in the literature: depth scaling 3,4 and antialiasing filtering. The former scales the depth at which details are presented and so distorts depth perception.…”

Section: Introductionmentioning

confidence: 99%

A locally content-dependent filter for inter-perspective anti-aliasing

et al. 2012

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Presentations on multiview and lightfield displays have become increasingly popular. The restricted number of views implies an unsmooth transition between views if objects with sharp edges are far from the display plane. The phenomenon is explained by inter-perspective aliasing. This is undesirable in applications where a correct perception of the scene is required, such as in science and medicine. Anti-aliasing filters have been proposed in the literature, and are defined according to the minimum and maximum depth present in the scene. We suggest a method that subdivides the ray-space and adjusts the anti-aliasing filter to the scene contents locally. We further propose new filter kernels based on the ray space frequency domain that assures no aliasing, yet keeping maximum information unaltered. The proposed method outperforms filters of earlier works. Different filter kernels are compared. Details of the output are sharper using a proposed filter kernel, which also preserves the most information.

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Depth Scaling of Multiview Images for Automultiscopic 3D Monitors

Cited by 7 publications

References 3 publications

Light Field Image Processing: An Overview

Light Field Image Processing: An Overview

Multi-perspective stereoscopy from light fields

A locally content-dependent filter for inter-perspective anti-aliasing

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