基于高维度特征分析的非局部图像质量评价方法

丁勇; 李楠

doi:10.11999/JEIT151430

基于高维度特征分析的非局部图像质量评价方法

doi: 10.11999/JEIT151430

丁勇¹,
李楠¹

基金项目:

国家863计划(2015AA016704c)，浙江省自然科学基金(LY14F020028)

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- 被引次数: 0
出版历程
- 收稿日期: 2015-12-17
- 修回日期: 2016-04-19
- 刊出日期: 2016-09-19

Image Quality Assessment Based on Non-localHigh Dimensional Feature Analysis

DING Yong¹,
LI Nan¹

Funds:

Items: The National 863 Program of China (2015AA016704c), Zhejiang Provincial Natural Science Foundation (LY14F020028)

摘要

摘要: 传统的图像质量评价方法通常提取低维度特征即图像的片面信息用来分析图像质量。高维度特征尽管不易分析但保留了更多信息，更利于全面分析图像质量。针对这种现状，该文提出一种优化数据采样后基于高维度特征分析的图像质量评价方法。首先对图像数据采样分别利用块匹配进行筛选，用主成分分析进行降维，其次利用核独立分量分析从图像数据采样中提取高维度特征，最后基于自然图像统计特性对特征进行综合得出图像质量。实验结果表明所提方法与人的主观评价较为一致。
- 图像质量评价 /
- 高维度特征 /
- 非局部 /
- 核独立分量分析
Abstract: Traditionally, low dimensional features for partial information are extracted to analyze image quality. Though high dimensional features are difficult to be analyzed, they contain more information to fully analyze image quality. On this condition, this paper proposes an image quality assessment method based on non-local high dimensional feature analysis after optimized data sampling. Firstly, image data is filtered by using block matching method and dimensionally reduced by Principal Component Analysis (PCA). Secondly, Kernel Independent Component Analysis (KICA) is applied to extract high dimensional features. The features are finally synthesized to evaluate image quality based on natural image statistics. The experimental results show that the proposed method keeps accordance with human objective perception.
- Image quality assessment /
- High dimensional feature /
- Non-local /
- Kernel Independent Component Analysis (KICA)

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参考文献(14)

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