Quantitative error analysis of bilateral filtering
One of the fastest acceleration techniques for bilateral image filtering is the real time $O(1)$ quantization method proposed by Yang 2009, which first computes some Principal Bilateral Filtered Image Components (PBFICs) and then applies linear interpolation to estimate the filtered output images. T...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Institute of Electrical and Electronics Engineers
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/69751 |
| _version_ | 1848762125144555520 |
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| author | An, Senjian Boussaid, F. Bennamoun, M. Sohel, F. |
| author_facet | An, Senjian Boussaid, F. Bennamoun, M. Sohel, F. |
| author_sort | An, Senjian |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | One of the fastest acceleration techniques for bilateral image filtering is the real time $O(1)$ quantization method proposed by Yang 2009, which first computes some Principal Bilateral Filtered Image Components (PBFICs) and then applies linear interpolation to estimate the filtered output images. There is a trade-off between accuracy and efficiency in selecting the number of PBFICs: the more PBFICs are used, the higher the accuracy, and the higher the computational cost. A question arises: how many PBFICs are required to achieve a certain level of accuracy? In this letter, we address this question by investigating the properties of bilateral filtering and deriving the linear interpolation error bounds when only a subset of PBFICs is used. The provided theoretical analysis indicates that the necessary number of PBFICs for user-provided precision depends on the range kernel and, for typical Gaussian range kernels, a small percentage (typically less than 4%) of the PBFICs are enough for good approximations. |
| first_indexed | 2025-11-14T10:42:35Z |
| format | Journal Article |
| id | curtin-20.500.11937-69751 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:42:35Z |
| publishDate | 2015 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-697512019-01-24T02:43:45Z Quantitative error analysis of bilateral filtering An, Senjian Boussaid, F. Bennamoun, M. Sohel, F. One of the fastest acceleration techniques for bilateral image filtering is the real time $O(1)$ quantization method proposed by Yang 2009, which first computes some Principal Bilateral Filtered Image Components (PBFICs) and then applies linear interpolation to estimate the filtered output images. There is a trade-off between accuracy and efficiency in selecting the number of PBFICs: the more PBFICs are used, the higher the accuracy, and the higher the computational cost. A question arises: how many PBFICs are required to achieve a certain level of accuracy? In this letter, we address this question by investigating the properties of bilateral filtering and deriving the linear interpolation error bounds when only a subset of PBFICs is used. The provided theoretical analysis indicates that the necessary number of PBFICs for user-provided precision depends on the range kernel and, for typical Gaussian range kernels, a small percentage (typically less than 4%) of the PBFICs are enough for good approximations. 2015 Journal Article http://hdl.handle.net/20.500.11937/69751 10.1109/LSP.2014.2353694 Institute of Electrical and Electronics Engineers restricted |
| spellingShingle | An, Senjian Boussaid, F. Bennamoun, M. Sohel, F. Quantitative error analysis of bilateral filtering |
| title | Quantitative error analysis of bilateral filtering |
| title_full | Quantitative error analysis of bilateral filtering |
| title_fullStr | Quantitative error analysis of bilateral filtering |
| title_full_unstemmed | Quantitative error analysis of bilateral filtering |
| title_short | Quantitative error analysis of bilateral filtering |
| title_sort | quantitative error analysis of bilateral filtering |
| url | http://hdl.handle.net/20.500.11937/69751 |