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Benchmarking of several disparity estimation algorithms for light field processing

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publicationFourteenth International Conference on Quality Control by Artificial Vision
EditorsStephane Bazeille, Nicolas Verrier, Christophe Cudel
PublisherSPIE, IEEE
ISBN (Electronic)9781510630536
Publication statusPublished - 2019
Publication typeA4 Article in a conference publication
EventInternational Conference on Quality Control by Artificial Vision - Mulhouse, France
Duration: 15 May 201917 May 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceInternational Conference on Quality Control by Artificial Vision


A number of high-quality depth imaged-based rendering (DIBR) pipelines have been developed to reconstruct a 3D scene from several images taken from known camera viewpoints. Due to the specific limitations of each technique, their output is prone to artifacts. Therefore, the quality cannot be ensured. To improve the quality of the most critical and challenging image areas, an exhaustive comparison is required. In this paper, we consider three questions of benchmarking the quality performance of eight DIBR techniques on light fields: First, how does the density of original input views affect the quality of the rendered novel views? Second, how does disparity range between adjacent input views impact the quality? Third, how does each technique behave for different object properties? We compared and evaluated the results visually as well as quantitatively (PSNR, SSIM, AD, and VDP2). The results show some techniques outperform others in different disparity ranges. The results also indicate using more views not necessarily results in visually higher quality for all critical image areas. Finally, we have shown a comparison for different scene's complexity such as non-Lambertian objects.


  • Depth image-based rendering, Disparity estimation, Quality evaluation

Publication forum classification

Field of science, Statistics Finland