Subjective similarity evaluation for scenic bilevel images
In order to provide ground truth for subjectively comparing compression methods for scenic bilevel images, as well as for judging objective similarity metrics, this paper describes the subjective similarity rating of a collection of distorted scenic bilevel images. Unlike text, line drawings, and si...
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| Vydané v: | Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 156 - 160 |
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| Hlavní autori: | , , |
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| Jazyk: | English |
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01.05.2014
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| ISSN: | 1520-6149 |
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| Abstract | In order to provide ground truth for subjectively comparing compression methods for scenic bilevel images, as well as for judging objective similarity metrics, this paper describes the subjective similarity rating of a collection of distorted scenic bilevel images. Unlike text, line drawings, and silhouettes, scenic bilevel images contain natural scenes, e.g., landscapes and portraits. Seven scenic images were each distorted in forty-four ways, including random bit flipping, dilation, erosion and lossy compression. To produce subjective similarity ratings, the distorted images were each viewed by 77 subjects. These are then used to compare the performance of four compression algorithms and to assess how well percentage error and SmSIM work as bilevel image similarity metrics. These subjective ratings can also provide ground truth for future tests of objective bilevel image similarity metrics. |
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| AbstractList | In order to provide ground truth for subjectively comparing compression methods for scenic bilevel images, as well as for judging objective similarity metrics, this paper describes the subjective similarity rating of a collection of distorted scenic bilevel images. Unlike text, line drawings, and silhouettes, scenic bilevel images contain natural scenes, e.g., landscapes and portraits. Seven scenic images were each distorted in forty-four ways, including random bit flipping, dilation, erosion and lossy compression. To produce subjective similarity ratings, the distorted images were each viewed by 77 subjects. These are then used to compare the performance of four compression algorithms and to assess how well percentage error and SmSIM work as bilevel image similarity metrics. These subjective ratings can also provide ground truth for future tests of objective bilevel image similarity metrics. |
| Author | Yuanhao Zhai Pappas, Thrasyvoulos N. Neuhoff, David L. |
| Author_xml | – sequence: 1 surname: Yuanhao Zhai fullname: Yuanhao Zhai organization: EECS Dept., Univ. of Michigan, Ann Arbor, MI, USA – sequence: 2 givenname: David L. surname: Neuhoff fullname: Neuhoff, David L. organization: EECS Dept., Univ. of Michigan, Ann Arbor, MI, USA – sequence: 3 givenname: Thrasyvoulos N. surname: Pappas fullname: Pappas, Thrasyvoulos N. organization: EECS Dept., Northwestern Univ., Evanston, IL, USA |
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| Snippet | In order to provide ground truth for subjectively comparing compression methods for scenic bilevel images, as well as for judging objective similarity metrics,... |
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| SubjectTerms | bilevel image similarity Compression algorithms Encoding Image coding image quality Measurement subjective evaluation Testing |
| Title | Subjective similarity evaluation for scenic bilevel images |
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