Digital images contain a lot of redundancies, therefore, compression techniques are applied to reduce the image size without loss of reasonable image quality. Same become more prominent in the case of videos which contains image sequences and higher compression ratios are achieved in low throughput networks. Assessment of quality of images in such scenarios has become of particular interest. Subjective evaluation in most of the scenarios is infeasible so objective evaluation is preferred. Among the three objective quality measures, full-reference and reduced-reference methods require an original image in some form to calculate the image quality which is unfeasible in scenarios such as broadcasting, acquisition or enhancement. Therefore, a no-reference Perceptual Image Quality Index (PIQI) is proposed in this paper to assess the quality of digital images which calculates luminance and gradient statistics along with mean subtracted contrast normalized products in multiple scales and color spaces. These extracted features are provided to a stacked ensemble of Gaussian Process Regression (GPR) to perform the perceptual quality evaluation. The performance of the PIQI is checked on six benchmark databases and compared with twelve state-of-the-art methods and competitive results are achieved. The comparison is made based on RMSE, Pearson and Spearman correlation coefficients between ground truth and predicted quality scores. The scores of 0.0552, 0.9802 and 0.9776 are achieved respectively for these metrics on CSIQ database. Two cross-dataset evaluation experiments are performed to check the generalization of PIQI.

翻译：数字图像包含大量冗余信息，因此采用压缩技术在保持合理图像质量的前提下减小图像尺寸。对于包含图像序列的视频，这一特性更为突出，在低吞吐量网络中可实现更高的压缩比。此类场景下的图像质量评估已成为重点关注领域。由于主观评价在多数情况下不可行，客观评价成为优先选择。在三种客观质量评价方法中，全参考和半参考方法均需要以某种形式的原始图像来计算图像质量，这在广播、采集或增强等场景中难以实现。为此，本文提出一种无参考感知图像质量指数（PIQI），通过在多尺度和多色彩空间中计算亮度统计量、梯度统计量以及均值减除对比度归一化乘积，提取图像特征。将这些特征输入由高斯过程回归（GPR）构成的堆叠集成模型，进行感知质量评估。我们在六个基准数据库上检验了PIQI的性能，并与十二种当前最优方法进行对比，取得了具有竞争力的结果。基于CSIQ数据库，以地面真值与预测质量分数之间的均方根误差（RMSE）、皮尔逊相关系数和斯皮尔曼相关系数为指标，分别取得了0.0552、0.9802和0.9776的分数。此外，还通过两项跨数据集评估实验验证了PIQI的泛化能力。