Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education of China, School of Electronic Engineering, Xidian University, Xi’an 710071, China.
IEEE Trans Image Process. 2013 Jan;22(1):43-54. doi: 10.1109/TIP.2012.2214048. Epub 2012 Aug 17.
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.
客观图像质量评估（IQA）旨在与人类感知一致地评估图像质量。现有的大多数感知 IQA 指标无法准确表示来自不同类型失真的劣化，例如，现有的结构相似性指标在内容相关的失真上表现良好，而在内容无关的失真上不如峰值信噪比（PSNR）。在本文中，我们结合了现有 IQA 指标的优点，并以最近揭示的内部生成机制（IGM）为指导。IGM 表明，人类视觉系统主动预测感觉信息，并试图避免对图像感知和理解的剩余不确定性。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部分，即具有预测视觉内容的预测部分和具有剩余内容的无序部分。预测部分的失真会降低主要视觉信息，并且采用结构相似性程序来测量其劣化；无序部分的失真主要改变不确定信息，并且采用 PNSR 对其进行测量。最后，根据两部分的噪声能量分配，我们结合这两个评估结果来获得整体质量得分。在六个公开可用的数据库上进行的实验结果表明，所提出的度量与最先进的质量度量相当。
