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Image quality assessment using natural scene statistics.

机译：使用自然场景统计数据进行图像质量评估。

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Measurement of image quality is crucial for designing image processing systems that could potentially degrade visual quality. Such measurements allow developers to optimize designs to deliver maximum quality while minimizing system cost. This dissertation is about automatic algorithms for quality assessment of digital images.; Traditionally, researchers have equated image quality with image fidelity, or the closeness of a distorted image to a 'reference' image that is assumed to have perfect quality. This closeness is typically measured by modeling the human visual system, or by using different mathematical criteria for signal similarity.; In this dissertation, I approach the problem from a novel direction. I claim that quality assessment algorithms deal only with images and videos that are meant for human consumption, and that these signals are almost exclusively images and videos of the visual environment. Image distortions make these so-called natural scenes look 'unnatural'. I claim that this departure from 'expected' characteristics could be quantified for predicting visual quality.; I present a novel information-theoretic approach to image quality assessment using statistical models for natural scenes. I approach the quality assessment problem as an information fidelity problem, in which the distortion process is viewed as a channel that limits the flow of information from a source of natural images to the receiver (the brain). I show that quality of a test image is strongly related to the amount of statistical information about the reference image that is present in the test image.; I also explore image quality assessment in the absence of the reference, and present a novel method for blindly quantifying the quality of images compressed by wavelet based compression algorithms. I show that images are rendered unnatural by the quantization process during lossy compression, and that this unnaturalness could be quantified blindly for predicting visual quality.; I test and validate the performance of the algorithms proposed in this dissertation through an extensive study in which ground truth data was obtained from many human subjects. I show that the methods presented can accurately predict visual quality, and that they outperform current state-of-the-art methods in my simulations.
机译：图像质量的测量对于设计可能会降低视觉质量的图像处理系统至关重要。此类测量使开发人员可以优化设计以提供最高质量，同时将系统成本降至最低。本文是关于数字图像质量评估的自动算法。传统上，研究人员将图像质量与图像保真度或失真图像与假定具有完美质量的“参考”图像的接近程度等同起来。通常通过对人类视觉系统建模或通过使用不同的数学准则来确定信号的相似性来测量这种接近度。本文从一个新的方向探讨了这个问题。我声称质量评估算法仅处理供人食用的图像和视频，并且这些信号几乎完全是视觉环境的图像和视频。图像失真使这些所谓的自然场景看起来“不自然”。我声称可以将这种与“预期”特征的偏离量化为预测视觉质量。我提出了一种使用自然场景统计模型的新颖信息理论方法来进行图像质量评估。我将质量评估问题视为信息保真度问题，其中失真过程被视为限制从自然图像源到接收器（大脑）的信息流的通道。我表明测试图像的质量与测试图像中存在的有关参考图像的统计信息量密切相关。我还探讨了在没有参考文献的情况下的图像质量评估，并提出了一种新颖的方法，用于盲目量化由基于小波的压缩算法压缩的图像的质量。我证明了在有损压缩过程中，量化过程会使图像变得不自然，并且可以通过盲目地量化这种不自然来预测视觉质量。我通过广泛的研究来测试和验证本文提出的算法的性能，该研究从许多人类受试者那里获得了地面真实数据。我证明了所提出的方法可以准确地预测视觉质量，并且在我的模拟中，它们的性能优于当前的最新方法。

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