Blind panoramic image quality assessment (BPIQA) has recently brought new challenge to the visual quality community, due to the complex interaction between immersive content and human behavior. Although many efforts have been made to advance BPIQA from both conducting psychophysical experiments and designing performance-driven objective algorithms, \textit{limited content} and \textit{few samples} in those closed sets inevitably would result in shaky conclusions, thereby hindering the development of BPIQA, we refer to it as the \textit{easy-database} issue. In this paper, we present a sufficient computational analysis of degradation modeling in BPIQA to thoroughly explore the \textit{easy-database issue}, where we carefully design three types of experiments via investigating the gap between BPIQA and blind image quality assessment (BIQA), the necessity of specific design in BPIQA models, and the generalization ability of BPIQA models. From extensive experiments, we find that easy databases narrow the gap between the performance of BPIQA and BIQA models, which is unconducive to the development of BPIQA. And the easy databases make the BPIQA models be closed to saturation, therefore the effectiveness of the associated specific designs can not be well verified. Besides, the BPIQA models trained on our recently proposed databases with complicated degradation show better generalization ability. Thus, we believe that much more efforts are highly desired to put into BPIQA from both subjective viewpoint and objective viewpoint.

翻译：盲全景图像质量评估（BPIQA）由于沉浸式内容与人类行为之间复杂的相互作用，最近给视觉质量研究领域带来了新的挑战。尽管通过开展心理物理实验和设计性能驱动的客观算法，已付出诸多努力来推进BPIQA的发展，但那些封闭数据集中的\textit{有限内容}和\textit{少量样本}不可避免地会导致结论的不稳定性，从而阻碍BPIQA的发展，我们称之为\textit{简易数据库}问题。本文对BPIQA中的退化建模进行了充分的计算分析，以深入探究\textit{简易数据库问题}。我们通过研究BPIQA与盲图像质量评估（BIQA）之间的差距、BPIQA模型中特定设计的必要性以及BPIQA模型的泛化能力，精心设计了三类实验。大量实验表明，简易数据库缩小了BPIQA与BIQA模型性能之间的差距，这不利于BPIQA的发展。此外，简易数据库使BPIQA模型性能趋于饱和，因此相关特定设计的有效性无法得到充分验证。同时，在我们最近提出的包含复杂退化的数据库上训练的BPIQA模型表现出更好的泛化能力。因此，我们认为，从主观和客观两个视角，都亟需对BPIQA投入更多的研究努力。