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Reduced-reference image quality assessment using energy change in reorganized DCT domain

机译：使用重组DCT域中的能量变化进行参考图像质量降低的评估

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Reduced-reference (RR) image quality assessment (IQA) intends to utilize less information of the reference image and yield higher evaluation accuracy. In this paper, a novel RR-IQAmetric that measures differences between the energy in reorganized discrete cosine (RDCT) domain of reference and distorted images as perceived by Human is presented. Firstly, we decompose an image into ten sub-bands in this new frequency domain. Since RDCT representation exhibits structural similarities between sub-bands, and can mimic the function of Human Visual System (HVS). Secondly, we extract features from the ten sub-bands by analyzing what are the key elements that influence subjective quality. Finally, we fuse these extracted features based on the principal that we exert different importance in accordance with the different impact each individual subband plays on the perceptual quality. Experimental results demonstrate that the proposedmetric outperforms the state-of-the-art (RR) IQAmetrics and even the full-reference (FR) IQA metrics SVD and SSIM. What is more, compared with many existing RR IQAs, the proposed metric earns obvious superiority in terms of the amount of its required information fromreference image and computational complexity.
机译：缩减参考（RR）图像质量评估（IQA）旨在利用参考图像的较少信息，并获得更高的评估精度。在本文中，提出了一种新颖的RR-IQAmetric，它可以测量参考的重组离散余弦（RDCT）域中的能量与人为扭曲图像之间的能量差。首先，我们在这个新的频域中将图像分解为十个子带。由于RDCT表示在子带之间表现出结构相似性，并且可以模仿人类视觉系统（HVS）的功能。其次，通过分析影响主观质量的关键因素，从十个子带中提取特征。最后，我们根据每个子带对感知质量的不同影响发挥不同重要性的原理融合这些提取的特征。实验结果表明，提出的指标优于最新的（RR）IQAmetrics，甚至优于全参考（FR）IQA指标SVD和SSIM。而且，与许多现有的RR IQA相比，该提议的度量在从参考图像获得的所需信息量和计算复杂性方面均具有明显的优势。

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