Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/599409
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTodd Richard Goodallen_US
dc.contributor.authorAlan Conrad Boviken_US
dc.contributor.authorNicholas G. Paulteren_US
dc.date.accessioned2020-05-20T08:54:06Z-
dc.date.available2020-05-20T08:54:06Z-
dc.date.issued2016en_US
dc.identifier.issn1057-7149en_US
dc.identifier.issn1941-0042en_US
dc.identifier.other10.1109/TIP.2015.2496289en_US
dc.identifier.urihttp://localhost/handle/Hannan/185867en_US
dc.identifier.urihttp://localhost/handle/Hannan/599409-
dc.description.abstractNatural scene statistics (NSSs) provide powerful, perceptually relevant tools that have been successfully used for image quality analysis of visible light images. Since NSS capture statistical regularities that arise from the physical world, they are relevant to long wave infrared (LWIR) images, which differ from visible light images mainly by the wavelengths captured at the imaging sensors. We show that NSS models of bandpass LWIR images are similar to those of visible light images, but with different parameterizations. Using this difference, we exploit the power of NSS to successfully distinguish between LWIR images and visible light images. In addition, we study distortions unique to LWIR and find directional models useful for detecting the halo effect, simple bandpass models useful for detecting hotspots, and combinations of these models useful for measuring the degree of non-uniformity present in many LWIR images. For local distortion identification and measurement, we also describe a method for generating distortion maps using NSS features. To facilitate our evaluation, we analyze the NSS of LWIR images under pristine and distorted conditions, using four databases, each captured with a different IR camera. Predicting human performance for assessing distortion and quality in LWIR images is critical for task efficacy. We find that NSS features improve human targeting task performance prediction. Furthermore, we conducted a human study on the perceptual quality of noise-and blur-distorted LWIR images and create a new blind image quality predictor for IR images.en_US
dc.publisherIEEEen_US
dc.relation.haspart7312989.pdfen_US
dc.subjectTTP|“Halo effect”|NU|NSS|LWIR|hotspoten_US
dc.titleTasking on Natural Statistics of Infrared Imagesen_US
dc.typeArticleen_US
dc.journal.volume25en_US
dc.journal.issue1en_US
dc.journal.titleIEEE Transactions on Image Processingen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7312989.pdf5.22 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTodd Richard Goodallen_US
dc.contributor.authorAlan Conrad Boviken_US
dc.contributor.authorNicholas G. Paulteren_US
dc.date.accessioned2020-05-20T08:54:06Z-
dc.date.available2020-05-20T08:54:06Z-
dc.date.issued2016en_US
dc.identifier.issn1057-7149en_US
dc.identifier.issn1941-0042en_US
dc.identifier.other10.1109/TIP.2015.2496289en_US
dc.identifier.urihttp://localhost/handle/Hannan/185867en_US
dc.identifier.urihttp://localhost/handle/Hannan/599409-
dc.description.abstractNatural scene statistics (NSSs) provide powerful, perceptually relevant tools that have been successfully used for image quality analysis of visible light images. Since NSS capture statistical regularities that arise from the physical world, they are relevant to long wave infrared (LWIR) images, which differ from visible light images mainly by the wavelengths captured at the imaging sensors. We show that NSS models of bandpass LWIR images are similar to those of visible light images, but with different parameterizations. Using this difference, we exploit the power of NSS to successfully distinguish between LWIR images and visible light images. In addition, we study distortions unique to LWIR and find directional models useful for detecting the halo effect, simple bandpass models useful for detecting hotspots, and combinations of these models useful for measuring the degree of non-uniformity present in many LWIR images. For local distortion identification and measurement, we also describe a method for generating distortion maps using NSS features. To facilitate our evaluation, we analyze the NSS of LWIR images under pristine and distorted conditions, using four databases, each captured with a different IR camera. Predicting human performance for assessing distortion and quality in LWIR images is critical for task efficacy. We find that NSS features improve human targeting task performance prediction. Furthermore, we conducted a human study on the perceptual quality of noise-and blur-distorted LWIR images and create a new blind image quality predictor for IR images.en_US
dc.publisherIEEEen_US
dc.relation.haspart7312989.pdfen_US
dc.subjectTTP|“Halo effect”|NU|NSS|LWIR|hotspoten_US
dc.titleTasking on Natural Statistics of Infrared Imagesen_US
dc.typeArticleen_US
dc.journal.volume25en_US
dc.journal.issue1en_US
dc.journal.titleIEEE Transactions on Image Processingen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7312989.pdf5.22 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTodd Richard Goodallen_US
dc.contributor.authorAlan Conrad Boviken_US
dc.contributor.authorNicholas G. Paulteren_US
dc.date.accessioned2020-05-20T08:54:06Z-
dc.date.available2020-05-20T08:54:06Z-
dc.date.issued2016en_US
dc.identifier.issn1057-7149en_US
dc.identifier.issn1941-0042en_US
dc.identifier.other10.1109/TIP.2015.2496289en_US
dc.identifier.urihttp://localhost/handle/Hannan/185867en_US
dc.identifier.urihttp://localhost/handle/Hannan/599409-
dc.description.abstractNatural scene statistics (NSSs) provide powerful, perceptually relevant tools that have been successfully used for image quality analysis of visible light images. Since NSS capture statistical regularities that arise from the physical world, they are relevant to long wave infrared (LWIR) images, which differ from visible light images mainly by the wavelengths captured at the imaging sensors. We show that NSS models of bandpass LWIR images are similar to those of visible light images, but with different parameterizations. Using this difference, we exploit the power of NSS to successfully distinguish between LWIR images and visible light images. In addition, we study distortions unique to LWIR and find directional models useful for detecting the halo effect, simple bandpass models useful for detecting hotspots, and combinations of these models useful for measuring the degree of non-uniformity present in many LWIR images. For local distortion identification and measurement, we also describe a method for generating distortion maps using NSS features. To facilitate our evaluation, we analyze the NSS of LWIR images under pristine and distorted conditions, using four databases, each captured with a different IR camera. Predicting human performance for assessing distortion and quality in LWIR images is critical for task efficacy. We find that NSS features improve human targeting task performance prediction. Furthermore, we conducted a human study on the perceptual quality of noise-and blur-distorted LWIR images and create a new blind image quality predictor for IR images.en_US
dc.publisherIEEEen_US
dc.relation.haspart7312989.pdfen_US
dc.subjectTTP|“Halo effect”|NU|NSS|LWIR|hotspoten_US
dc.titleTasking on Natural Statistics of Infrared Imagesen_US
dc.typeArticleen_US
dc.journal.volume25en_US
dc.journal.issue1en_US
dc.journal.titleIEEE Transactions on Image Processingen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7312989.pdf5.22 MBAdobe PDFThumbnail
Preview File