The satisfied user ratio (SUR) for a given distortion level is the fraction of subjects that cannot perceive a quality difference between the original image and its compressed version. By predicting the SUR, one can determine the highest distortion level which allows to save bit rate while guaranteeing a good visual quality. We propose the first method to predict the SUR for symmetrically and asymmetrically compressed stereoscopic images. Unlike SUR prediction techniques for two-dimensional images and videos, our method exploits the properties of binocular vision. We first extract features that characterize image quality and image content. Then, we use gradient boosting decision trees to reduce the number of features and train a regression model that learns a mapping function from the features to the SUR values. Experimental results on the SIAT-JSSI and SIAT-JASI datasets show high SUR prediction accuracy for H.265 All-Intra and JPEG2000 symmetrically and asymmetrically compressed stereoscopic images.

中文翻译：

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基于学习的对称和非对称压缩立体图像的满意用户比预测

给定失真水平的满意用户比率 (SUR) 是无法感知原始图像与其压缩版本之间质量差异的对象的比例。通过预测 SUR，人们可以确定最高失真级别，从而在保证良好视觉质量的同时节省比特率。我们提出了第一种方法来预测对称和非对称压缩立体图像的 SUR。与二维图像和视频的 SUR 预测技术不同，我们的方法利用了双目视觉的特性。我们首先提取表征图像质量和图像内容的特征。然后，我们使用梯度提升决策树来减少特征数量并训练回归模型，该模型学习从特征到 SUR 值的映射函数。