Reduced-Reference Image Quality Assessment in Free-Energy Principle and Sparse Representation
Reduced-Reference Image Quality Assessment in Free-Energy Principle and Sparse Representation
复制标题
DOI：
10.1109/tmm.2017.2729020
复制
发表时间：
2018-02-01
影响因子：
7.3
通讯作者：
Gao, Wen
中科院分区：
文献类型：
--
作者：
Liu, Yutao;Zhai, Guangtao;Gao, Wen
最近大脑理论和神经科学研究中的自由能原理将外部场景的感知和理解建模为一个主动的推理过程，其中大脑试图用内部生成模型来解释视觉场景。具体来说，通过内部生成模型，大脑对其遇到的视觉场景产生相应的预测。那么，视觉输入与其大脑预测之间的差异应该与感知的质量密切相关。另一方面，稀疏表示已被证明类似于大脑中初级视觉皮层表示自然图像的策略。借助稀疏表示强大的神经生物学支持，本文用稀疏表示逼近内部生成模型，并相应地提出了一种图像质量度量，称为FSI（基于自由能原理和稀疏表示的图像质量评估指数）。在FSI中，首先通过稀疏表示分别预测参考图像和失真图像。然后，定义预测差异的熵之间的差来测量图像质量。四个大型图像数据库的实验结果证实了 FSI 的有效性及其相对于代表性图像质量评估方法的优越性。 FSI属于简化参考方法，只需要参考图像中的单个数字即可进行质量估计。
The free-energy principle in recent studies of brain theory and neuroscience models the perception and understanding of the outside scene as an active inference process, in which the brain tries to account for the visual scene with an internal generative model. Specifically, with the internal generative model, the brain yields corresponding predictions for its encountered visual scenes. Then, the discrepancy between the visual input and its brain prediction should be closely related to the quality of perceptions. On the other hand, sparse representation has been evidenced to resemble the strategy of the primary visual cortex in the brain for representing natural images. With the strong neurobiological support for sparse representation, in this paper, we approximate the internal generative model with sparse representation and propose an image quality metric accordingly, which is named FSI (free-energy principle and sparse representation-based index for image quality assessment). In FSI, the reference and distorted images are, respectively, predicted by the sparse representation at first. Then, the difference between the entropies of the prediction discrepancies is defined to measure the image quality. Experimental results on four large-scale image databases confirm the effectiveness of the FSI and its superiority over representative image quality assessment methods. The FSI belongs to reduced-reference methods, and it only needs a single number from the reference image for quality estimation.