As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology.

In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning ‘quality-aware’ features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of ‘quality-aware’ features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model.

近年来，随着对高质量立体图像的需求不断增长，立体图像质量评估（SIQA）已成为现代图像处理技术的重要研究领域。

在本文中，我们提出了一种无参考立体图像质量评估 (NR-SIQA) 模型，该模型通过四元数小波变换 (QWT) 使用来自亮度图像、色度图像、视差和独眼图像的异构集成学习“质量感知”特征。首先，通过CIELAB色彩空间生成亮度图像和色度图像作为单目感知，并利用新颖的视差和独眼图像与单目信息进行补充。然后，发现了四元数小波域中的许多“质量感知”特征，包括熵、纹理特征、能量特征、能量差异特征和高频子带的MSCN系数。最后，通过支持向量回归（SVR）和极限学习机（ELM）的异构集成模型 提出了随机森林（RF）来预测质量分数，并使用自举采样和旋转特征空间来增加数据分布的多样性。与最先进的 NR-SIQA 模型相比，四个公共数据库的实验结果证明了所提出模型的准确性和鲁棒性。