Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/644997
Title: Backward Registration-Based Aspect Ratio Similarity for Image Retargeting Quality Assessment
Authors: Yabin Zhang;Yuming Fang;Weisi Lin;Xinfeng Zhang;Leida Li
subject: backward registration|Image retargeting quality assessment|geometric change
Year: 2016
Publisher: IEEE
Abstract: During the past few years, there have been various kinds of content-aware image retargeting operators proposed for image resizing. However, the lack of effective objective retargeting quality assessment metrics limits the further development of image retargeting techniques. Different from traditional image quality assessment (IQA) metrics, the quality degradation during image retargeting is caused by artificial retargeting modifications, and the difficulty for image retargeting quality assessment (IRQA) lies in the alternation of the image resolution and content, which makes it impossible to directly evaluate the quality degradation like traditional IQA. In this paper, we interpret the image retargeting in a unified framework of resampling grid generation and forward resampling. We show that the geometric change estimation is an efficient way to clarify the relationship between the images. We formulate the geometric change estimation as a backward registration problem with Markov random field and provide an effective solution. The geometric change aims to provide the evidence about how the original image is resized into the target image. Under the guidance of the geometric change, we develop a novel aspect ratio similarity (ARS) metric to evaluate the visual quality of retargeted images by exploiting the local block changes with a visual importance pooling strategy. Experimental results on the publicly available MIT RetargetMe and CUHK data sets demonstrate that the proposed ARS can predict more accurate visual quality of retargeted images compared with the state-of-the-art IRQA metrics.
URI: http://localhost/handle/Hannan/177434
http://localhost/handle/Hannan/644997
ISSN: 1057-7149
1941-0042
volume: 25
issue: 9
Appears in Collections:2016

Files in This Item:
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7501594.pdf6.56 MBAdobe PDFThumbnail
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Title: Backward Registration-Based Aspect Ratio Similarity for Image Retargeting Quality Assessment
Authors: Yabin Zhang;Yuming Fang;Weisi Lin;Xinfeng Zhang;Leida Li
subject: backward registration|Image retargeting quality assessment|geometric change
Year: 2016
Publisher: IEEE
Abstract: During the past few years, there have been various kinds of content-aware image retargeting operators proposed for image resizing. However, the lack of effective objective retargeting quality assessment metrics limits the further development of image retargeting techniques. Different from traditional image quality assessment (IQA) metrics, the quality degradation during image retargeting is caused by artificial retargeting modifications, and the difficulty for image retargeting quality assessment (IRQA) lies in the alternation of the image resolution and content, which makes it impossible to directly evaluate the quality degradation like traditional IQA. In this paper, we interpret the image retargeting in a unified framework of resampling grid generation and forward resampling. We show that the geometric change estimation is an efficient way to clarify the relationship between the images. We formulate the geometric change estimation as a backward registration problem with Markov random field and provide an effective solution. The geometric change aims to provide the evidence about how the original image is resized into the target image. Under the guidance of the geometric change, we develop a novel aspect ratio similarity (ARS) metric to evaluate the visual quality of retargeted images by exploiting the local block changes with a visual importance pooling strategy. Experimental results on the publicly available MIT RetargetMe and CUHK data sets demonstrate that the proposed ARS can predict more accurate visual quality of retargeted images compared with the state-of-the-art IRQA metrics.
URI: http://localhost/handle/Hannan/177434
http://localhost/handle/Hannan/644997
ISSN: 1057-7149
1941-0042
volume: 25
issue: 9
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7501594.pdf6.56 MBAdobe PDFThumbnail
Preview File
Title: Backward Registration-Based Aspect Ratio Similarity for Image Retargeting Quality Assessment
Authors: Yabin Zhang;Yuming Fang;Weisi Lin;Xinfeng Zhang;Leida Li
subject: backward registration|Image retargeting quality assessment|geometric change
Year: 2016
Publisher: IEEE
Abstract: During the past few years, there have been various kinds of content-aware image retargeting operators proposed for image resizing. However, the lack of effective objective retargeting quality assessment metrics limits the further development of image retargeting techniques. Different from traditional image quality assessment (IQA) metrics, the quality degradation during image retargeting is caused by artificial retargeting modifications, and the difficulty for image retargeting quality assessment (IRQA) lies in the alternation of the image resolution and content, which makes it impossible to directly evaluate the quality degradation like traditional IQA. In this paper, we interpret the image retargeting in a unified framework of resampling grid generation and forward resampling. We show that the geometric change estimation is an efficient way to clarify the relationship between the images. We formulate the geometric change estimation as a backward registration problem with Markov random field and provide an effective solution. The geometric change aims to provide the evidence about how the original image is resized into the target image. Under the guidance of the geometric change, we develop a novel aspect ratio similarity (ARS) metric to evaluate the visual quality of retargeted images by exploiting the local block changes with a visual importance pooling strategy. Experimental results on the publicly available MIT RetargetMe and CUHK data sets demonstrate that the proposed ARS can predict more accurate visual quality of retargeted images compared with the state-of-the-art IRQA metrics.
URI: http://localhost/handle/Hannan/177434
http://localhost/handle/Hannan/644997
ISSN: 1057-7149
1941-0042
volume: 25
issue: 9
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7501594.pdf6.56 MBAdobe PDFThumbnail
Preview File