Intensity-sensitive similarity indexes for image quality assessment
The importance of Image quality assessment (IQA) is ever increasing due to the fast paced advances in imaging technology and computer vision. Among the numerous IQA methods, Structural SIMilarity (SSIM) index and its variants are better matched to the perceived quality of the human visual system. Ho...
| Main Authors: | , |
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| Format: | Conference item |
| Language: | English |
| Published: |
IEEE
2022
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| _version_ | 1826309177946931200 |
|---|---|
| author | Li, X Armour, W |
| author_facet | Li, X Armour, W |
| author_sort | Li, X |
| collection | OXFORD |
| description | The importance of Image quality assessment (IQA)
is ever increasing due to the fast paced advances in imaging
technology and computer vision. Among the numerous IQA
methods, Structural SIMilarity (SSIM) index and its variants
are better matched to the perceived quality of the human visual
system. However, SSIM methods are insufficiently sensitive, when
images contain low information, where the important information
only occupies a low proportion of the image while most of
the image is noise-like, which is common in scientific data.
Therefore, we propose two new IQA methods, InTensity Weighted
SSIM index and Low-Information Similarity Index, for such low
information images. In addition, auxiliary indexes are proposed
to assist with the assessment. The application of these new IQA
methods to natural images and field-specific images, such as
radio astronomical images, medical images, and remote sensing
images, are also demonstrated. The results show that our IQA
methods perform better than state-of-the-art SSIM methods
for differences in high-intensity parts of the input images and
have similar performance to that of the original and gradientbased SSIM for differences in low-intensity parts. Different
similarity indexes are suitable for different applications, which
we demonstrate in our results. |
| first_indexed | 2024-03-07T07:30:18Z |
| format | Conference item |
| id | oxford-uuid:62c8557e-bc20-4844-b984-758599cc68a8 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:30:18Z |
| publishDate | 2022 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:62c8557e-bc20-4844-b984-758599cc68a82023-01-20T10:25:11ZIntensity-sensitive similarity indexes for image quality assessmentConference itemhttp://purl.org/coar/resource_type/c_5794uuid:62c8557e-bc20-4844-b984-758599cc68a8EnglishSymplectic ElementsIEEE2022Li, XArmour, WThe importance of Image quality assessment (IQA) is ever increasing due to the fast paced advances in imaging technology and computer vision. Among the numerous IQA methods, Structural SIMilarity (SSIM) index and its variants are better matched to the perceived quality of the human visual system. However, SSIM methods are insufficiently sensitive, when images contain low information, where the important information only occupies a low proportion of the image while most of the image is noise-like, which is common in scientific data. Therefore, we propose two new IQA methods, InTensity Weighted SSIM index and Low-Information Similarity Index, for such low information images. In addition, auxiliary indexes are proposed to assist with the assessment. The application of these new IQA methods to natural images and field-specific images, such as radio astronomical images, medical images, and remote sensing images, are also demonstrated. The results show that our IQA methods perform better than state-of-the-art SSIM methods for differences in high-intensity parts of the input images and have similar performance to that of the original and gradientbased SSIM for differences in low-intensity parts. Different similarity indexes are suitable for different applications, which we demonstrate in our results. |
| spellingShingle | Li, X Armour, W Intensity-sensitive similarity indexes for image quality assessment |
| title | Intensity-sensitive similarity indexes for image quality assessment |
| title_full | Intensity-sensitive similarity indexes for image quality assessment |
| title_fullStr | Intensity-sensitive similarity indexes for image quality assessment |
| title_full_unstemmed | Intensity-sensitive similarity indexes for image quality assessment |
| title_short | Intensity-sensitive similarity indexes for image quality assessment |
| title_sort | intensity sensitive similarity indexes for image quality assessment |
| work_keys_str_mv | AT lix intensitysensitivesimilarityindexesforimagequalityassessment AT armourw intensitysensitivesimilarityindexesforimagequalityassessment |