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Image quality assessment by an efficient correlation-based metric
Concurrency and Computation: Practice and Experience ( IF 2 ) Pub Date : 2020-04-28 , DOI: 10.1002/cpe.5794
Li‐Hui Lin 1, 2 , Tzong‐Jer Chen 3
Affiliation  

Image quality can be measured visually. In the human visual system, a compressed image can be judged by the human eye. Image quality may not be perceived to decline in a region with low compression. However, image quality clearly declines in a region with high compression. As image compression increases, image quality gradually transitions from visually lossless to lossy. In this study, we aim to explain this phenomenon. A few images from different datasets were selected and compressed using JJ2000 and Apollo, which are well‐known image compression algorithms. Then, error‐based and correlation‐based metrics were applied to these images. The correlation‐based metrics agree with human‐vision evaluations in experiments, but the error‐based metrics do not. Inspired by the positive result of the correlation‐based metrics, a new metric named the simple correlation factor (SCF) was proposed to explain the aforementioned phenomenon. The results of the SCF show good consistency with human‐vision results for several datasets. In addition, the computation efficiency of the SCF is better than that of the existing correlation‐based metrics.

中文翻译:

通过有效的基于相关性的指标进行图像质量评估

图像质量可以通过视觉测量。在人类视觉系统中,压缩后的图像可以通过人眼来判断。在低压缩率的区域中可能不会感觉到图像质量下降。然而,图像质量在高压缩区域明显下降。随着图像压缩率的增加,图像质量逐渐从视觉无损过渡到有损。在这项研究中,我们旨在解释这一现象。使用 JJ2000 和 Apollo 选择和压缩来自不同数据集的一些图像,它们是众所周知的图像压缩算法。然后,将基于误差和基于相关性的指标应用于这些图像。基于相关性的指标与实验中的人类视觉评估一致,但基于误差的指标则不然。受到基于相关性指标的积极结果的启发,提出了一种名为简单相关因子 (SCF) 的新指标来解释上述现象。SCF 的结果与几个数据集的人类视觉结果显示出良好的一致性。此外,SCF 的计算效率优于现有的基于相关性的度量。
更新日期:2020-04-28
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