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首页> 外文期刊>International Journal of Wavelets, Multiresolution and Information Processing >Image quality degradation assessment based on the dual-tree complex discrete wavelet transform for evaluating watermarked images
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Image quality degradation assessment based on the dual-tree complex discrete wavelet transform for evaluating watermarked images

机译:基于双树复杂离散小波变换评估水印图像的图像质量退化评估

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摘要

We propose a new image quality degradation assessment method based on the dual-tree complex discrete wavelet transform (DT-CDWT) for evaluating the image quality of watermarked images. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are widely used to evaluate image quality degradation resulting from embedding a digital watermark. The majority of digital image watermarking methods embed a digital watermark in the spatial or frequency domain of an original image. They evaluate image quality degradation using only the spatial domain in spite of the fact that the majority of digital image watermarking methods embed a digital watermark in the spatial or frequency domain. As a result, they do not always fairly evaluate the image quality degradation. Therefore, our method evaluates image quality degradation of the watermarked images using features in the spatial and frequency domains. To extract the features, we defined three indices: 1-norm estimation using bit-planes in the spatial domain, the sharpness, and 1-norm estimation based on the DT-CDWT domains. We describe our image quality assessment method in detail and present experimental results demonstrating that there is a strong positive correlation between the result obtained by our method and a subjective evaluation, in comparison with PSNR and SSIM.
机译:我们提出了一种基于双树复杂离散小波变换(DT-CDWT)的新的图像质量退化评估方法,用于评估水印图像的图像质量。峰值信噪比(PSNR)和结构相似度(SSIM)被广泛用于评估嵌入数字水印产生的图像质量劣化。大多数数字图像水印方法在原始图像的空间或频域中嵌入了数字水印。它们仅在空间域中评估图像质量劣化,尽管大多数数字图像水印方法在空间或频域中嵌入数字水印。结果,它们并不总是相当评估图像质量退化。因此,我们的方法使用空间和频域中的特征评估水印图像的图像质量劣化。要提取特征,我们使用空间域中的比特平面,基于DT-CDWT域的尖平,锐度和1常态估计来定义三个指标:1常规估计。我们详细描述了我们的图像质量评估方法,并存在实验结果表明,与PSNR和SSIM相比,通过我们的方法获得的结果和主观评估之间存在强烈的正相关性。

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