Image Quality Assessment Based on Non-localHigh Dimensional Feature Analysis

DING Yong; LI Nan

doi:10.11999/JEIT151430

Volume 38 Issue 9

Sep. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(9): 2365-2370

DING Yong, LI Nan. Image Quality Assessment Based on Non-localHigh Dimensional Feature Analysis[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2365-2370. doi: 10.11999/JEIT151430

Citation:

DING Yong, LI Nan. Image Quality Assessment Based on Non-localHigh Dimensional Feature Analysis[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2365-2370. doi: 10.11999/JEIT151430

DING Yong, LI Nan. Image Quality Assessment Based on Non-localHigh Dimensional Feature Analysis[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2365-2370. doi: 10.11999/JEIT151430

Citation:

DING Yong, LI Nan. Image Quality Assessment Based on Non-localHigh Dimensional Feature Analysis[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2365-2370. doi: 10.11999/JEIT151430

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Image Quality Assessment Based on Non-localHigh Dimensional Feature Analysis

doi: 10.11999/JEIT151430 cstr: 32379.14.JEIT151430

DING Yong¹,
LI Nan¹

Funds:

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

Received Date: 2015-12-17
Rev Recd Date: 2016-04-19
Publish Date: 2016-09-19

Abstract

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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References(14)

References

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