The objective of blind-image quality assessment (BIQA) research is the prediction of perceptual quality of images, without reference information. The human’s perceptual assessment of quality of an image is the backbone of BIQA research. Therefore, human-provided, mean opinion score (perceptual quality) has been analyzed in detail, and it has been observed to follow the Gaussian distribution and thus can be ideally modeled by the same. In this paper, we have proposed an integrated two-stage Gaussian process-based hybrid-feature selection algorithm for the BIQA problem. Moreover, a new consolidated feature set (obtained from the proposed algorithm), consisting of momentous Natural Scene Statistics (NSS)-based features is used in combination with the Gaussian process regression algorithm for the design of a new blind-image quality evaluator, referred to as GPR-BIQA. The proposed evaluator is tested on eight IQA legacy databases, and it is found that the proposed evaluator proficiently correlate with the human opinion, and outperformed a substantial number of existing approaches.

盲图像质量评估（BIQA）研究的目的是在没有参考信息的情况下预测图像的感知质量。人对图像质量的感知评估是BIQA研究的基础。因此，已经对人类提供的平均意见得分（感知质量）进行了详细分析，并且观察到它遵循高斯分布，因此可以理想地用高斯分布建模。在本文中，我们针对BIQA问题提出了一种基于集成两阶段高斯过程的混合特征选择算法。此外，新的合并特征集（从提出的算法中获得）由基于瞬时自然景物统计（NSS）的特征组成，并与高斯过程回归算法结合使用，用于设计新的盲图质量评估器，简称为GPR-BIQA。该提议的评估者在八个IQA旧数据库上进行了测试，发现该提议的评估者与人的意见充分相关，并且胜过许多现有方法。