Image information and visual quality
Image information and visual quality
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DOI：
10.1109/tip.2005.859378
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发表时间：
2006-02-01
影响因子：
10.6
通讯作者：
Bovik, AC
中科院分区：
文献类型：
--
作者：
Sheikh, HR;Bovik, AC
视觉质量的测量对于众多图像和视频处理应用至关重要。质量评估（QA）研究的目的是设计算法，可以自动以感知一致的方式自动评估图像或视频的质量。图像QA算法通常将图像质量解释为在某些感知空间中使用“参考”或“完美”图像的忠诚度或相似性。这种“全参考”质量检查方法试图通过建模人类视觉系统（HVS）的显着生理和心理特征，或通过信号保真度度量来实现质量预测的一致性。在本文中，我们将图像质量检查问题作为信息保真度问题。具体而言，我们建议将图像信息丢失量化为失真过程，并探索图像信息与视觉质量之间的关系。质量保证系统总是参与判断旨在“人类消费”的“自然”图像和视频的视觉质量。研究人员开发了复杂的模型来捕获这种自然信号的统计数据。使用这些模型，我们先前提出了图像质量质量质量的信息保真度标准，该标准与参考图像和扭曲的图像之间共享的信息量相关。在本文中，我们提出了一项图像信息度量，该图像量量化了参考图像中存在的信息，以及可以从扭曲的图像中提取此参考信息中的多少。结合了这两个数量，我们提出了图像质量质量质量图的视觉信息保真度度量。我们通过一项涉及779张图像的广泛主观研究来验证算法的性能，并表明我们的方法在模拟中的差距相当大的余量优于最新的最新图像QA算法。主观研究的代码和数据可在Live网站上获得。
Measurement of visual quality is of fundamental importance to numerous image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. Image QA algorithms generally interpret image quality as fidelity or similarity with a "reference" or "perfect" image in some perceptual space. Such "full-reference" QA methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychovisual features of the human visual system (HVS), or by signal fidelity measures. In this paper, we approach the image QA problem as an information fidelity problem. Specifically, we propose to quantify the loss of image information to the distortion process and explore the relationship between image information and visual quality. QA systems are invariably involved with judging the visual quality of "natural" images and videos that are meant for "human consumption." Researchers have developed sophisticated models to capture the statistics of such natural signals. Using these models, we previously presented an information fidelity criterion for image QA that related image quality with the amount of information shared between a reference and a distorted image. In this paper, we propose an image information measure that quantifies the information that is present in the reference image and how much of this reference information can be extracted from the distorted image. Combining these two quantities, we propose a visual information fidelity measure for image QA. We validate the performance of our algorithm with an extensive subjective study involving 779 images and show that our method outperforms recent state-of-the-art image QA algorithms by a sizeable margin in our simulations. The code and the data from the subjective study are available at the LIVE website.