The human visual system (HVS) is a hierarchical system, in which visual signals are processed hierarchically. In this paper, the HVS is modeled as a three-level communication system and visual perception is divided into three stages according to the hierarchical predictive coding theory. Then, a novel just noticeable distortion (JND) estimation scheme is proposed. In visual perception, the input signals are predicted constantly and spontaneously in each hierarchy, and neural response is evoked by the central residue and inhibited by surrounding residues. These two types’ residues are regarded as the positive and negative visual incentives which cause positive and negative perception effects, respectively. In neuroscience, the effect of incentive on observer is measured by the surprise of this incentive. Thus, we propose a surprise-based measurement method to measure both perception effects. Specifically, considering the biased competition of visual attention, we define the product of the residue self-information (i.e., surprise) and the competition biases as the perceptual surprise to measure the positive perception effect. As for the negative perception effect, it is measured by the average surprise (i.e., the local Shannon entropy). The JND threshold of each stage is estimated individually by considering both perception effects. The total JND threshold is finally obtained by non-linear superposition of three stage thresholds. Furthermore, the proposed JND estimation scheme is incorporated into the codec of Versatile Video Coding for image compression. Experimental results show that the proposed JND model outperforms the relevant existing ones, and over 16% of bit rate can be reduced without jeopardizing the perceptual quality.

中文翻译：

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基于分层预测编码的图像压缩JND估计

人类视觉系统（HVS）是一个分层系统，其中视觉信号被分层处理。本文将HVS建模为三层通信系统，根据层次预测编码理论将视觉感知分为三个阶段。然后，提出了一种新颖的恰到好处的失真（JND）估计方案。在视觉感知中，在每个层次结构中不断且自发地预测输入信号，并且中央残基引起神经反应，周围残基抑制神经反应。这两种类型的残留物分别被视为正面和负面的视觉诱因，分别导致正面和负面的感知效果。在神经科学中，激励对观察者的影响通过这种激励的惊奇来衡量。从而，我们提出了一种基于惊喜的测量方法来测量两种感知效果。具体来说，考虑到视觉注意力的偏向竞争，我们将残差自我信息（即惊奇）和竞争偏向的乘积定义为衡量积极知觉效果的感知惊奇。至于负面知觉效应，它是通过平均惊喜（即局部香农熵）来衡量的。每个阶段的JND阈值都是通过考虑两种感知效果来单独估计的。最后，通过三级阈值的非线性叠加获得总JND阈值。此外，将所提出的JND估计方案结合到用于图像压缩的通用视频编码的编解码器中。实验结果表明，提出的JND模型优于现有的相关模型，