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A ParaBoost stereoscopic image quality assessment (PBSIQA) system

机译：ParaBoost立体图像质量评估（PBSIQA）系统

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摘要

The problem of stereoscopic image quality assessment, which finds applications in 3D visual content delivery such as 3DTV, is investigated in this work. Specifically, we propose a new ParaBoost (parallel boosting) stereoscopic image quality assessment (PBSIQA) system. The system consists of two stages. In the first stage, various distortions are classified into a few types, and individual quality scorers targeting at a specific distortion type are developed. These scorers offer complementary performance in face of a database consisting of heterogeneous distortion types. In the second stage, scores from multiple quality scorers are fused to achieve the best overall performance, where the fuser is designed based on the parallel boosting idea borrowed from machine learning. Extensive experimental results are conducted to compare the performance of the proposed PBSIQA system with those of existing stereo image quality assessment (SIQA) metrics. The developed quality metric can serve as an objective function to optimize the performance of a 3D content delivery system. (C) 2017 Elsevier Inc. All rights reserved.
机译：在这项工作中，研究了立体图像质量评估问题，该问题在3D视觉内容交付（例如3DTV）中得到了应用。具体来说，我们提出了一种新的ParaBoost（并行增强）立体图像质量评估（PBSIQA）系统。该系统包括两个阶段。在第一阶段，将各种失真分类为几种类型，并针对特定失真类型开发了单独的质量评分器。面对由异构失真类型组成的数据库，这些评分器可提供互补的性能。在第二阶段，融合来自多个质量得分手的得分以实现最佳整体性能，其中融合器是基于从机器学习中借鉴的并行增强思想而设计的。进行了广泛的实验结果，以比较建议的PBSIQA系统的性能与现有的立体图像质量评估（SIQA）指标的性能。开发的质量度量可以用作优化3D内容交付系统性能的目标功能。 （C）2017 Elsevier Inc.保留所有权利。

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