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Image quality assessment based on inter-patch and intra-patch similarity.

Zhou F, Lu Z, Wang C, Sun W, Xia ST, Liao Q - PLoS ONE (2015)

Bottom Line: According to local image contents, the intra-patch similarity is measured by adaptively comparing image curvature and gradient.Besides, a nonlinear integration of the inter-patch and intra-patch similarity is presented to obtain an overall score of image quality.The experiments conducted on six publicly available image databases show that our scheme achieves better performance in comparison with several state-of-the-art schemes.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronic Engineering, Tsinghua University, Beijing, 10084, China; The Shenzhen Key Laboratory of Information Science & Technology/Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China.

ABSTRACT
In this paper, we propose a full-reference (FR) image quality assessment (IQA) scheme, which evaluates image fidelity from two aspects: the inter-patch similarity and the intra-patch similarity. The scheme is performed in a patch-wise fashion so that a quality map can be obtained. On one hand, we investigate the disparity between one image patch and its adjacent ones. This disparity is visually described by an inter-patch feature, where the hybrid effect of luminance masking and contrast masking is taken into account. The inter-patch similarity is further measured by modifying the normalized correlation coefficient (NCC). On the other hand, we also attach importance to the impact of image contents within one patch on the IQA problem. For the intra-patch feature, we consider image curvature as an important complement of image gradient. According to local image contents, the intra-patch similarity is measured by adaptively comparing image curvature and gradient. Besides, a nonlinear integration of the inter-patch and intra-patch similarity is presented to obtain an overall score of image quality. The experiments conducted on six publicly available image databases show that our scheme achieves better performance in comparison with several state-of-the-art schemes.

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Overall framework of the proposed IQA scheme.
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pone.0116312.g001: Overall framework of the proposed IQA scheme.

Mentions: Similar to many existing schemes, we first split images into patches so that a graphical map with distortion measures at each pixel position can be obtained. In the simplified expression, each image patch can be represented by its center pixel. Then, we measure the similarity of the inter-patch and intra-patch features, respectively. Finally, we adaptively integrate the results of the two portions into one single score. The overall framework of the proposed IQA scheme is illustrated in Fig. 1, where some blocks will be introduced in detail in the following.


Image quality assessment based on inter-patch and intra-patch similarity.

Zhou F, Lu Z, Wang C, Sun W, Xia ST, Liao Q - PLoS ONE (2015)

Overall framework of the proposed IQA scheme.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4368764&req=5

pone.0116312.g001: Overall framework of the proposed IQA scheme.
Mentions: Similar to many existing schemes, we first split images into patches so that a graphical map with distortion measures at each pixel position can be obtained. In the simplified expression, each image patch can be represented by its center pixel. Then, we measure the similarity of the inter-patch and intra-patch features, respectively. Finally, we adaptively integrate the results of the two portions into one single score. The overall framework of the proposed IQA scheme is illustrated in Fig. 1, where some blocks will be introduced in detail in the following.

Bottom Line: According to local image contents, the intra-patch similarity is measured by adaptively comparing image curvature and gradient.Besides, a nonlinear integration of the inter-patch and intra-patch similarity is presented to obtain an overall score of image quality.The experiments conducted on six publicly available image databases show that our scheme achieves better performance in comparison with several state-of-the-art schemes.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronic Engineering, Tsinghua University, Beijing, 10084, China; The Shenzhen Key Laboratory of Information Science & Technology/Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China.

ABSTRACT
In this paper, we propose a full-reference (FR) image quality assessment (IQA) scheme, which evaluates image fidelity from two aspects: the inter-patch similarity and the intra-patch similarity. The scheme is performed in a patch-wise fashion so that a quality map can be obtained. On one hand, we investigate the disparity between one image patch and its adjacent ones. This disparity is visually described by an inter-patch feature, where the hybrid effect of luminance masking and contrast masking is taken into account. The inter-patch similarity is further measured by modifying the normalized correlation coefficient (NCC). On the other hand, we also attach importance to the impact of image contents within one patch on the IQA problem. For the intra-patch feature, we consider image curvature as an important complement of image gradient. According to local image contents, the intra-patch similarity is measured by adaptively comparing image curvature and gradient. Besides, a nonlinear integration of the inter-patch and intra-patch similarity is presented to obtain an overall score of image quality. The experiments conducted on six publicly available image databases show that our scheme achieves better performance in comparison with several state-of-the-art schemes.

Show MeSH