Image Quality Evaluation Model Based on Local Features and Segmentation
Image Quality Evaluation Model Based on Local Features and Segmentation
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DOI：
10.1109/icip.2006.312479
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发表时间：
2006-10
期刊：
影响因子：
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通讯作者：
Y. Horita;Masaharu Sato;Yoshikazu Kawayoke;Parvez Z. M. Sazzad;K. Shibata
中科院分区：
文献类型：
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作者：
Y. Horita;Masaharu Sato;Yoshikazu Kawayoke;Parvez Z. M. Sazzad;K. Shibata
任何图像的感知失真都强烈地依赖于图像的局部特征，如边缘、平坦和纹理。本文提出了一种新的基于局部特征和分割的JPEG编码图像客观无参考（NR）质量评价模型，该模型计算简单，适用于各种图像处理应用。但本文对分割算法的阈值研究不够充分。因此，在本文中，我们要探讨我们的分割算法的训练和测试图像的优化方法的合适的阈值。我们对各种图像失真类型的实验表明，它的性能显着优于传统的模型。
The perceived image distortion of any image is strongly depend on the local features, such as edge, flat and texture. A new objective no-reference (NR) image quality evaluation model based on local features and segmentation for JPEG coded image is presented in our previous paper, which is easy to calculate and applicable to various image processing applications. But the algorithmic thresholds investigation of the segmentation algorithm were not sufficient in the paper. Therefore in this paper, we want to investigate the suitable threshold values of our segmentation algorithm both for training and test images by the optimization method. Our experiments on various image distortion types indicate that its performs significantly better than the conventional model.