原语熵:一种自顶向下评估感性视觉信息的方法

Xianguo Zhang, Shiqi Wang, Siwei Ma, Shaohui Liu, Wen Gao
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引用次数: 20

摘要

本文基于一种新颖的自顶向下的方法:原始熵(EoP)来评估感知视觉信息。EoP由描述图像的原子分布决定,并被证明与感知图像质量密切相关。在视觉信息评价的基础上,我们进一步证明了EoP在预测自然图像的感知无损方面是有效的。受此启发,为了区分输入信号的损失对人类视觉系统(HVS)是否可见,我们引入了基于EoP的感知无损轮廓(PLP)。大量实验证明,本文提出的基于EoP的感知无损轮廓可以有效地测量最小可注意视觉信息失真,并且与最新的刚可注意差分(JND)轮廓相比,具有更好的性能。
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Entropy of primitive: A top-down methodology for evaluating the perceptual visual information
In this paper, we aim at evaluating the perceptual visual information based on a novel top-down methodology: entropy of primitive (EoP). The EoP is determined by the distribution of the atoms in describing an image, and is demonstrated to exhibit closely correlation with the perceptual image quality. Based on the visual information evaluation, we further demonstrate that the EoP is effective in predicting the perceptual lossless of natural images. Inspired by this observation, in order to distinguish whether the loss of input signal is visual noticeable to human visual system (HVS), we introduce the EoP based perceptual lossless profile (PLP). Extensive experiments verify that, the proposed EoP based perceptual lossless profile can efficiently measure the minimum noticeable visual information distortion and achieve better performance compared to the-state-of-the-art just-noticeable difference (JND) profile.
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