This paper presents a rotation-invariant and computationally efficient no-reference image quality assessment (NR-IQA) model. It estimates the image quality based on texture and structural information associated with the images. The human visual system (HVS) uses perceptual features such as texture and structure as primary information to understand the visual scene and the image content. Moreover, the texture and structural information capture the loss of naturalness due to distortions in the image. Therefore, in this work, the important texture features are extracted using the local binary patterns (LBP).

The modified LBP, also known as the hyper-smoothing LBP (H-LBP) and Laplacian of H-LBP (LH-LBP), represents the image structure. Further, the image quality prediction model computes the quality of the image based on the statistical feature measures of the texture and structural information. In the proposed approach, the image quality prediction model uses support vector regression (SVR) to measure image quality. Various experimentations are carried out on the LIVE and TID2013 database to test the effectiveness of the proposed NR-IQA model. The performance metrics such as Spearman rank-ordered correlation coefficient, Pearson linear correlation coefficient, and root mean square error is computed to show the efficiency of the presented approach. The experimental results illustrate a high correlation between the predicted quality score and the human visual perceptions. It is also found to be competitive with the best-performing full-reference and no-reference IQA models.

本文提出了一种旋转不变且计算效率高的无参考图像质量评估 (NR-IQA) 模型。它根据与图像相关的纹理和结构信息来估计图像质量。人类视觉系统（HVS）使用诸如纹理和结构等感知特征作为主要信息来理解视觉场景和图像内容。此外，纹理和结构信息捕获了由于图像失真而导致的自然度损失。因此，在这项工作中，重要的纹理特征是使用局部二值模式（LBP）提取的。

修改后的 LBP，也称为超平滑 LBP (H-LBP) 和 H-LBP 的拉普拉斯算子 (LH-LBP)，表示图像结构。此外，图像质量预测模型基于纹理和结构信息的统计特征度量来计算图像的质量。在所提出的方法中，图像质量预测模型使用支持向量回归 (SVR) 来测量图像质量。在 LIVE 和 TID2013 数据库上进行了各种实验，以测试所提出的 NR-IQA 模型的有效性。计算性能指标，例如 Spearman 秩相关系数、Pearson 线性相关系数和均方根误差，以显示所提出方法的效率。实验结果表明预测的质量分数与人类视觉感知之间存在高度相关性。它还被发现与性能最佳的全参考和无参考 IQA 模型具有竞争力。