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A Full-Reference Image Quality Assessment Algorithm Based on Haar Wavelet Transform

机译:基于Haar小波变换的全参考图像质量评估算法

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This paper proposes a new objective full-reference (FR) image quality assessment method with discrete Haar wavelet transform (HWSSIM). Here, both reference and test images are decomposed into four bands by 2-D Haar wavelet transform: LL, HL, LH and HH. The luminance and contrast information are extracted in the LL band, and the edge structure information is obtained with edge information map based on other three bands and the whole image quality is defined as MHWSSIM. We also develop our algorithm on the multi-resolutions case (MRHWSSIM). which is defined as the human visual system (HVS) contrast sensitivity based weighted mean of four MHWSSIM values evaluated at four Haar wavelet decomposition levels. Experiment results indicate that the proposed methods are better correlated with HVS when compared to PSNR, MSSIM and MGSSIM.
机译:本文提出了一种新的具有离散Haar小波变换(HWSSIM)的客观全参考(FR)图像质量评估方法。在此,参考图像和测试图像都通过2-D Haar小波变换分解为四个波段:LL,HL,LH和HH。在LL频带中提取亮度和对比度信息,并基于其他三个频带利用边缘信息图获得边缘结构信息,并且将整个图像质量定义为MHWSSIM。我们还针对多分辨率情况(MRHWSSIM)开发了我们的算法。定义为基于人类视觉系统(HVS)对比敏感度的四个MHWSSIM值在四个Haar小波分解级别上评估的加权平均值。实验结果表明,与PSNR,MSSIM和MGSSIM相比,该方法与HVS具有更好的相关性。

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