In this paper, we propose an effective approach to infer the just noticeable distortion (JND) profile based on patch-level structural visibility learning. Instead of pixel-level JND profile estimation, the image patch, which is regarded as the basic processing unit to better correlate with the human perception, can be further decomposed into three conceptually independent components for visibility estimation. In particular, to incorporate the structural degradation into the patch-level JND model, a deep learning-based structural degradation estimation model is trained to approximate the masking of structural visibility. In order to facilitate the learning process, a JND dataset is further established, including 202 pristine images and 7878 distorted images generated by advanced compression algorithms based on the upcoming Versatile Video Coding (VVC) standard. Extensive experimental results further show the superiority of the proposed approach over the state-of-the-art. Our dataset is available at: https://github.com/ShenXuelin-CityU/PWJNDInfer .

中文翻译：

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刚注意到的失真轮廓推断：一种补丁级别的结构可见性学习方法

在本文中，我们提出了一种有效的方法，基于面片级结构可见性学习来推断正好可察觉的失真（JND）轮廓。代替像素级JND轮廓估计，可以将被视为与人类感知更好相关的基本处理单元的图像块进一步分解为三个概念独立的组件，以进行可见性估计。特别是，为了将结构退化合并到补丁程序级别的JND模型中，对基于深度学习的结构退化估计模型进行了训练，以近似掩盖结构可见性。为了促进学习过程，进一步建立了JND数据集，包括202张原始图像和由基于即将到来的通用视频编码（VVC）标准的高级压缩算法生成的7878张失真图像。大量的实验结果进一步表明，该方法优于最新技术。我们的数据集位于：https://github.com/ShenXuelin-CityU/PWJNDInfer 。