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首页> 外文期刊>Journal of visual communication & image representation >Image quality/distortion metric based on α-stable model similarity in wavelet domain
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Image quality/distortion metric based on α-stable model similarity in wavelet domain

机译：小波域基于α稳定模型相似度的图像质量/失真度量

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摘要

Quality assessment is of central importance in numerous image processing tasks. State-of-the-art objective image quality assessment (IQA) algorithms are generally devised for specific distortion types or based on training procedure of large databases. In this work, we propose a general-purpose full-reference/no-reference (FR/NR) IQA framework for image distortions, nominated by Image Quality/Distortion Metric (IQDM). The leptokurtic and heavy-tailed behaviors of image wavelet coefficients are characterized by symmetric α-stable (SαS) density, and the statistical studies indicate that the model parameters may be altered because of the presence of distortion. This important priori knowledge of original image's distribution is then used to gauge the distortion between degraded and reference SαS models in multi-scale wavelet sub-bands. We investigate the relationship between original and degraded parameters over scales, accordingly infer the original parameters from the degraded ones. A characteristic probability density function for SαS and its closed-form Kullback-Leibler distance are derived for FR/NR-IQDM using the model parameters. Extensive experiments and comparisons demonstrate that the proposed FR/NR-IQDM scheme is efficacious to most common types of distortion, and leads to a highly comparable performance to the benchmarks and prevalent competitors in consistency with subjective judgements.
机译：在许多图像处理任务中，质量评估至关重要。通常针对特定的失真类型或基于大型数据库的训练过程设计最新的客观图像质量评估（IQA）算法。在这项工作中，我们提出了一种用于图像失真的通用全参考/无参考（FR / NR）IQA框架，该框架由图像质量/失真度量标准（IQDM）提名。图像小波系数的轻快和重尾行为以对称的α稳定（SαS）密度为特征，并且统计研究表明，由于存在失真，模型参数可能会发生变化。然后，使用有关原始图像分布的重要先验知识，可以衡量多尺度小波子带中降级和参考SαS模型之间的失真。我们研究了原始参数和退化参数之间的关系，从而从退化参数中推断出原始参数。使用模型参数为FR / NR-IQDM导出SαS的特征概率密度函数及其闭合形式的Kullback-Leibler距离。大量的实验和比较表明，所提出的FR / NR-IQDM方案对大多数常见类型的失真均有效，并且在与主观判断一致的情况下，可与基准和普遍竞争者实现高度可比的性能。

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