In visual perception, the neural response is evoked by the central stimulus and inhibited by the surrounding stimuli. These two stimuli can be regarded as the positive and negative incentives for perception. In this letter, the aftereffect of two incentives is incorporated into the just noticeable distortion (JND) estimation to avoid the threshold overestimation issue. In neuroscience, how an incentive affects an observer is measured by the surprise of this incentive which is estimated by the residual self-information according to the effective coding theory. Considering the competition biases, we define the product of the residual self-information and the biases, named as perceptual surprise, to measure the positive effect. As for the negative effect, we measure it with the average surprise (i.e., local Shannon entropy). Besides, visual signals are processed hierarchically in the HVS. To simplify the JND modeling process, the HVS is divided into two levels and the perception is simply divided into two stages. In each stage, the threshold is estimated individually and the total JND threshold is obtained by non-linear superposition of two stage thresholds. Experimental results show that the proposed JND model outperforms the existing pixel-based JND models and effectively avoid the threshold overestimation issue.

中文翻译：

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基于惊喜的图像 JND 估计

在视觉感知中，神经反应由中枢刺激引起，被周围刺激抑制。这两种刺激可以看作是感知的正负激励。在这封信中，两种激励措施的后效被纳入刚好可察觉失真 (JND) 估计中，以避免阈值高估问题。在神经科学中，激励如何影响观察者是通过根据有效编码理论由剩余的自我信息估计的激励的惊喜来衡量的。考虑到竞争偏差，我们定义了剩余自我信息和偏差的乘积，称为感知惊喜，以衡量积极影响。至于负面影响，我们用平均惊喜（即局部香农熵）来衡量。除了，视觉信号在 HVS 中分层处理。为了简化 JND 建模过程，将 HVS 分为两个层次，简单地将感知分为两个阶段。在每个阶段，阈值是单独估计的，总 JND 阈值是通过两个阶段阈值的非线性叠加获得的。实验结果表明，所提出的 JND 模型优于现有的基于像素的 JND 模型，并有效避免了阈值高估问题。