In this paper, we address problems associated with free-energy-principle-based image quality assessment (IQA) algorithms for objectively assessing the quality of Screen Content (SC) and three-dimensional (3-D) synthesized images and also propose a very fast and efficient IQA algorithm to address these issues. These algorithms separate an image into predicted and disorder residual parts and assume disorder residual part does not contribute much to the overall perceptual quality. These algorithms fail for quality estimation of SC images as information of textual regions in SC images are largely separated into the disorder residual part and less information in the predicted part and subsequently, given a negligible emphasis. However, this is in contrast with the characteristics of human vision. Since our eyes are well trained to detect text in daily life. So, our human vision has prior information about text regions and can sense small distortions in these regions. In this paper, we proposed a new reduced-reference IQA algorithm for SC images based upon a more perceptually relevant prediction model and distortion categorization, which overcomes problems with existing free-energy-principle-based predictors. From experiments, it is validated that the proposed model has a better capability of efficiently estimating the quality of SC images as compared to the recently developed reduced-reference IQA algorithms. We also applied the proposed algorithm to judge the quality of 3-D synthesized images and observed that it even achieves better performance than the full-reference IQA metrics specifically designed for the 3-D synthesized views.

中文翻译：

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用于屏幕内容和 3D 合成图像质量评估的预测支持模型


在本文中，我们解决了与基于自由能原理的图像质量评估（IQA）算法相关的问题，以客观地评估屏幕内容（SC）和三维（3-D）合成图像的质量，并提出了一种非常有效的方法。快速高效的 IQA 算法可以解决这些问题。这些算法将图像分为预测残差部分和无序残差部分，并假设无序残差部分对整体感知质量贡献不大。这些算法无法用于 SC 图像的质量估计，因为 SC 图像中的文本区域信息很大程度上被分为无序残留部分和预测部分中的较少信息，并且随后给出的强调可以忽略不计。然而，这与人类视觉的特性相反。因为我们的眼睛经过良好的训练来检测日常生活中的文本。因此，我们的人类视觉具有有关文本区域的先验信息，并且可以感知这些区域中的微小扭曲。在本文中，我们提出了一种新的 SC 图像减少参考 IQA 算法，该算法基于更感知相关的预测模型和失真分类，克服了现有基于自由能原理的预测器的问题。实验证明，与最近开发的减少参考 IQA 算法相比，所提出的模型具有更好的有效估计 SC 图像质量的能力。我们还应用所提出的算法来判断 3D 合成图像的质量，并观察到它甚至比专门为 3D 合成视图设计的全参考 IQA 指标获得了更好的性能。