Image Quality Assessment Using Author Topic Model

Tianbing Zhang, Wang Luo
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引用次数: 1

Abstract

In this paper, we propose a novel no reference image quality assessment method. This method performs image quality assessment by incorporating a graphical model. To obtain the results of the image quality assessment, first, we use a set of pristine and distorted images without human subjective scores for training. Second, the images are represented by several quality-aware visual words that are based on natural scene statistic features. Third, author topic model is leveraged to estimate probability of topic for the regions in the test images. At last, the perceptual quality score of the whole image can be obtained by comparing the estimated probabilities of topics with the average distribution of topics for a large number of natural images. Experimental evaluation on the LIVE IQA database demonstrates that the proposed method correlates well with human difference mean opinion scores.
基于作者主题模型的图像质量评估
本文提出了一种新的无参考图像质量评价方法。该方法通过结合图形模型来执行图像质量评估。为了获得图像质量评估的结果,首先,我们使用一组原始和失真的图像进行训练,而不需要人为的主观评分。其次,使用基于自然场景统计特征的若干质量感知视觉词来表示图像。第三,利用作者主题模型估计测试图像中区域的主题概率。最后,将估计的主题概率与大量自然图像的主题平均分布进行比较,得到整幅图像的感知质量得分。在LIVE IQA数据库上进行的实验评估表明,该方法与人类差异平均意见得分具有良好的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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