Perceptual Quality Metric With Internal Generative Mechanism
Perceptual Quality Metric With Internal Generative Mechanism
复制标题
具有内部生成机制的感知质量指标
DOI：
10.1109/tip.2012.2214048
复制
发表时间：
2013-01-01
影响因子：
10.6
通讯作者：
Liu, Anmin
中科院分区：
文献类型：
--
作者：
Wu, Jinjian;Lin, Weisi;Liu, Anmin
客观图像质量评估（IQA）旨在评价图像质量与人类感知一致。现有的大多数感知IQA度量不能准确地表示来自不同类型失真的退化，例如。例如，在一个实施例中，现有的结构相似性度量在内容相关的失真上表现良好，而在内容无关的失真上表现不如峰值信噪比（PSNR）。在本文中，我们整合了现有的IQA指标的优点，最近发现的内部生成机制（IGM）的指导。IGM表明人类视觉系统主动预测感官信息，并试图避免图像感知和理解的残余不确定性。受IGM理论的启发，我们采用自回归预测算法将输入场景分解为两个部分，预测部分与预测的视觉内容和无序部分与残留内容。预测部分的畸变主要影响原始视觉信息，采用结构相似性方法对其进行度量;无序部分的畸变主要影响不确定信息，采用PNSR对其进行度量。最后，根据噪声能量在两部分的分布情况，将两部分的评价结果进行联合收割机综合，得到整体质量得分。在六个公开数据库上的实验结果表明，所提出的指标与最先进的质量指标相当。
Objective image quality assessment (IQA) aims to evaluate image quality consistently with human perception. Most of the existing perceptual IQA metrics cannot accurately represent the degradations from different types of distortion, e. g., existing structural similarity metrics perform well on content-dependent distortions while not as well as peak signal-to-noise ratio (PSNR) on content-independent distortions. In this paper, we integrate the merits of the existing IQA metrics with the guide of the recently revealed internal generative mechanism (IGM). The IGM indicates that the human visual system actively predicts sensory information and tries to avoid residual uncertainty for image perception and understanding. Inspired by the IGM theory, we adopt an autoregressive prediction algorithm to decompose an input scene into two portions, the predicted portion with the predicted visual content and the disorderly portion with the residual content. Distortions on the predicted portion degrade the primary visual information, and structural similarity procedures are employed to measure its degradation; distortions on the disorderly portion mainly change the uncertain information and the PNSR is employed for it. Finally, according to the noise energy deployment on the two portions, we combine the two evaluation results to acquire the overall quality score. Experimental results on six publicly available databases demonstrate that the proposed metric is comparable with the state-of-the-art quality metrics.