The just noticeable distortion (JND) in the contour and orderly regions is easy to be overestimated and that in the disorderly areas is usually underestimated. In order to estimate the JND threshold more accurately, this paper proposes an improved DCT-based JND estimation model considering multiple masking effects properly. The contributions of this paper are characterized by twofold. On the one hand, a mean absolute difference based (MAD-based) block classification method is developed at first to classify image blocks into plain, contour and texture types accurately and quickly. And the JND model for contrast masking effect (CM-JND) is constructed as a modulation factor based on the MAD of each block. On the other hand, we propose a distance-based disorder evaluation metric to measure the disorder intensity in block level. Then, the JND model for the disorderly concealment effect (DC-JND) is proposed based on our psychological experiment. Finally, the total JND estimation threshold is modeled by fusing the spatial contrast sensitivity function, the luminance adaptation effect, the CM and DC effects. Experimental results show that the proposed DCT-based JND estimation model outperforms existing models in performance and complexity. Specifically, the proposed model shows more tolerance for distortions, lower computational complexity with better perceptual quality than other JND models.

轮廓和有序区域中恰好明显的失真（JND）容易被高估，而无序区域中的失真通常通常被低估。为了更准确地估计JND阈值，本文提出了一种改进的基于DCT的JND估计模型，该模型适当地考虑了多种掩蔽效果。本文的贡献具有双重特征。一方面，首先开发了一种基于平均绝对差（基于MAD）的块分类方法，以将图像块准确，快速地分类为平原，轮廓和纹理类型。然后，基于每个块的MAD将用于对比度掩盖效果的JND模型（CM-JND）构造为调制因子。另一方面，我们提出了一种基于距离的障碍评估指标，以量度障碍水平的障碍程度。然后，基于我们的心理实验，提出了针对隐蔽性障碍的JND模型（DC-JND）。最后，通过融合空间对比度灵敏度函数，亮度适应效应，CM和DC效应对总JND估计阈值建模。实验结果表明，所提出的基于DCT的JND估计模型在性能和复杂度上均优于现有模型。具体而言，与其他JND模型相比，所提出的模型显示出更大的失真容忍度，更低的计算复杂度和更好的感知质量。实验结果表明，所提出的基于DCT的JND估计模型在性能和复杂度上均优于现有模型。具体而言，与其他JND模型相比，所提出的模型显示出更大的失真容忍度，更低的计算复杂度和更好的感知质量。实验结果表明，所提出的基于DCT的JND估计模型在性能和复杂度上均优于现有模型。具体而言，与其他JND模型相比，所提出的模型显示出更大的失真容忍度，更低的计算复杂度和更好的感知质量。