VALID: Visual quality Assessment for Light field Images Dataset

Irene Viola, T. Ebrahimi
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引用次数: 35

Abstract

In the last years, light field imaging has experienced a surge of popularity among the scientific community for its capability of rendering the 3D world in a more immersive way. In particular, several compression algorithms have been proposed to efficiently reduce the amount of data generated in the acquisition process, and different methodologies have been designed to reliably evaluate the visual quality of compressed contents. In this paper we propose a dataset for visual quality assessment of light field images (VALID). The dataset contains five contents compressed at various bitrates, using both off-the-shelf solutions and state-of-the-art algorithms. Results of objective quality evaluation using popular image metrics are included, as well as annotated subjective scores using three different methodologies and two types of visualization setups. The proposed dataset will help develop new objective metrics to predict visual quality, design new subjective assessment methodologies and compare them to existing ones, as well as produce novel analysis approaches to interpret the results.
有效:光场图像数据集的视觉质量评估
在过去的几年里,光场成像因其以更身临其境的方式渲染3D世界的能力而在科学界中受到了广泛的欢迎。特别是,已经提出了几种压缩算法来有效地减少采集过程中产生的数据量,并且已经设计了不同的方法来可靠地评估压缩内容的视觉质量。本文提出了一个用于光场图像视觉质量评估的数据集(VALID)。数据集包含以不同比特率压缩的五个内容,使用现成的解决方案和最先进的算法。包括使用流行图像指标的客观质量评估结果,以及使用三种不同方法和两种类型的可视化设置的注释主观评分。提出的数据集将有助于开发新的客观指标来预测视觉质量,设计新的主观评估方法并将其与现有方法进行比较,以及产生新的分析方法来解释结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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