The estimation of image quality and noise perception still remains an important issue in various image processing applications. It has also become a hot topic in the field of photo-realistic computer graphics where noise is inherent in the calculation process. Unlike natural-scene images, however, a reference image is not available for computer-generated images. Thus, classic methods to assess noise quantity and stopping criterion during the rendering process are not usable. This is particularly important in the case of global illumination methods based on stochastic techniques: They provide photo-realistic images which are, however, corrupted by stochastic noise. This noise can be reduced by increasing the number of paths, as proved by Monte Carlo theory, but the problem of finding the right number of paths that are required in order to ensure that human observers cannot perceive any noise is still open. Until now, the features taking part in the human evaluation of image quality and the remaining perceived noise are not precisely known. Synthetic image generation tends to be very expensive and the produced datasets are high-dimensional datasets. In that case, finding a stopping criterion using a learning framework is a challenging task. In this paper, a new method for characterizing computational noise for computer generated images is presented. The noise is represented by the entropy of the singular value decomposition of each block composing an image. These Singular Value Decomposition (SVD)-entropy values are then used as input to a recurrent neural network architecture model in order to extract image noise and in predicting a visual convergence threshold of different parts of any image. Thus a new no-reference image quality assessment is proposed using the relation between SVD-Entropy and perceptual quality, based on a sequence of distorted images. Experiments show that the proposed method, compared with experimental psycho-visual scores, demonstrates a good consistency between these scores and stopping criterion measures that we obtain.

中文翻译：

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基于SVD-Entropy的计算机生成图像渲染过程中的停止准则

图像质量和噪声感知的估计仍然是各种图像处理应用中的一个重要问题。它也成为计算过程中固有噪声的逼真计算机图形领域的热门话题。然而，与自然场景图像不同，参考图像不可用于计算机生成的图像。因此，在渲染过程中评估噪声量和停止标准的经典方法是不可用的。这在基于随机技术的全局照明方法的情况下尤为重要：它们提供逼真的图像，但是会被随机噪声破坏。这种噪声可以通过增加路径的数量来减少，正如蒙特卡罗理论所证明的那样，但是找到正确数量的路径以确保人类观察者无法感知任何噪音的问题仍然存在。到目前为止，参与人类对图像质量评估的特征和剩余的感知噪声尚不清楚。合成图像生成往往非常昂贵，并且生成的数据集是高维数据集。在这种情况下，使用学习框架找到停止标准是一项具有挑战性的任务。在本文中，提出了一种表征计算机生成图像的计算噪声的新方法。噪声由组成图像的每个块的奇异值分解的熵表示。然后将这些奇异值分解 (SVD) 熵值用作循环神经网络架构模型的输入，以提取图像噪声并预测任何图像不同部分的视觉收敛阈值。因此，基于一系列失真图像，使用 SVD 熵和感知质量之间的关系提出了一种新的无参考图像质量评估。实验表明，与实验心理视觉分数相比，所提出的方法表明这些分数与我们获得的停止标准度量之间具有良好的一致性。