Lightfield (LF) technology has attained significant attention in recent years due to its capability to capture much richer textural and geometric information in the scene compared to the classical 2D representation. The resampling and compression operations on LFs often lead to visual quality degradation, thus, sophisticated visual quality assessment methods play a crucial role to ensure a pleasant viewing experience. To this end, it is necessary to examine the performance of quality assessment methods for LF contents. The paper provides a comprehensive study on the reliability of various objective algorithms for LF quality prediction. Three subjectively-annotated LF data sets were selected and an extensive quality estimation analysis has been conducted using several objective quality assessment methods. In total, 250 LFs (more than 48000 perspective images) were evaluated. The results were compared against human opinion scores using various correlation indices and their statistical significance. Next, a decision-making strategy was adopted to choose the most reliable quality metrics for evaluation of LFs and finally, a metric fusion framework was proposed to further improve the quality prediction accuracy. To best of our knowledge, the benchmark and the analytical methodologies used in this paper are the most comprehensive study on the objective quality assessment methods for LF application.

由于与经典2D表示相比，光场（LF）技术能够捕获场景中更丰富的纹理和几何信息，因此近年来受到了广泛的关注。LF的重新采样和压缩操作通常会导致视觉质量下降，因此，复杂的视觉质量评估方法对于确保令人愉悦的观看体验起着至关重要的作用。为此，有必要检查LF含量的质量评估方法的性能。本文对各种用于低频质量预测的目标算法的可靠性进行了全面的研究。选择了三个主观注释的LF数据集，并使用几种客观的质量评估方法进行了广泛的质量评估分析。总共，评估了250个LF（超过48000个透视图）。使用各种相关指数及其统计意义，将结果与人类意见得分进行比较。接下来，采用决策策略选择最可靠的质量度量进行LF评估，最后提出了度量融合框架以进一步提高质量预测的准确性。据我们所知，本文所使用的基准和分析方法是对低频应用客观质量评估方法的最全面研究。采取决策策略选择最可靠的质量指标进行LFs评估，最后提出了一种指标融合框架，以进一步提高质量预测的准确性。据我们所知，本文所使用的基准和分析方法是对低频应用客观质量评估方法的最全面研究。采取决策策略选择最可靠的质量指标进行LFs评估，最后提出了一种指标融合框架，以进一步提高质量预测的准确性。据我们所知，本文所使用的基准和分析方法是对低频应用客观质量评估方法的最全面研究。