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An information theory of visual communication

1996; Royal Society; Volume: 354; Issue: 1716 Linguagem: Inglês

10.1098/rsta.1996.0098

1471-2962

Friedrich O. Huck, Carl L. Fales, Zia-ur Rahman,

Visual Attention and Saliency Detection

Restricted accessMoreSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail Cite this article Huck Friedrich O. , Fales Carl L. and Rahman Zia-ur 1996An information theory of visual communicationPhil. Trans. R. Soc. A.3542193–2248http://doi.org/10.1098/rsta.1996.0098SectionRestricted accessArticleAn information theory of visual communication Friedrich O. Huck Google Scholar Find this author on PubMed Search for more papers by this author , Carl L. Fales Google Scholar Find this author on PubMed Search for more papers by this author and Zia-ur Rahman Google Scholar Find this author on PubMed Search for more papers by this author Friedrich O. Huck Google Scholar Find this author on PubMed , Carl L. Fales Google Scholar Find this author on PubMed and Zia-ur Rahman Google Scholar Find this author on PubMed Published:15 October 1996https://doi.org/10.1098/rsta.1996.0098AbstractThe fundamental problem of visual communication is that of producing the best possible picture at the lowest data rate. We address this problem by extending information theory to the assessment of the visual communication channel as a whole, from image gathering to display. The extension unites two disciplines, the electro- optical design of image gathering and display devices and the digital processing for image coding and restoration. The mathematical development leads to several intuitively attractive figures of merit for assessing the visual communication channel as a function of the critical limiting factors that constrain its performance. Multiresolution decomposition is included in the mathematical development to optimally combine the economical encoding of the transmitted signal with image gathering and restoration. Quantitative and qualitative assessments demonstrate that a visual communication channel ordinarily can be expected to produce the best possible picture at the lowest data rate only if the image-gathering device produces the maximum-realizable information rate and the image-restoration algorithm properly accounts for the critical limiting factors that constrain the visual communication. These assessments encompass (a) the electro-optical design of the image-gathering device in terms of the trade-off between blurring and aliasing in the presence of photodetector and quantization noises, (b) the compression of data transmission by redundancy reduction, (c) the robustness of the image restoration to uncertainties in the statistical properties of the captured radiance field, and (d) the enhancement of particular features or, more generally, of the visual quality of the observed image. The 'best visual quality' in this context normally implies a compromise among maximum-realizable fidelity, sharpness, and clarity which depends on the characteristics of the scene and the purpose of the visual communication (e.g. diagnosis versus entertainment).FootnotesThis text was harvested from a scanned image of the original document using optical character recognition (OCR) software. As such, it may contain errors. Please contact the Royal Society if you find an error you would like to see corrected. Mathematical notations produced through Infty OCR. Next Article VIEW FULL TEXT DOWNLOAD PDF FiguresRelatedReferencesDetailsCited by Byrnes N and Foreman M (2020) Universal bounds for imaging in scattering media * , New Journal of Physics, 10.1088/1367-2630/aba063, 22:8, (083023), Online publication date: 1-Aug-2020. Zhang Y, Zhang J and Yang W (2020) Quantifying Information Content in Multispectral Remote-Sensing Images Based on Image Transforms and Geostatistical Modelling, Remote Sensing, 10.3390/rs12050880, 12:5, (880) Mait J, Euliss G and Athale R (2018) Computational imaging, Advances in Optics and Photonics, 10.1364/AOP.10.000409, 10:2, (409), Online publication date: 30-Jun-2018. 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Huck F (1999) Information-theoretic assessment of sampled imaging systems, Optical Engineering, 10.1117/1.602264, 38:5, (742), Online publication date: 1-May-1999. Park S (1999) Fidelity analysis of sampled imaging systems, Optical Engineering, 10.1117/1.602047, 38:5, (786), Online publication date: 1-May-1999. Huck F, Fales C and Rahman Z (1997) Introduction Visual Communication, 10.1007/978-1-4757-2568-1_1, (1-12), . Huck F, Fales C and Rahman Z On the information-theoretic assessment of visual communication 3rd IEEE International Conference on Image Processing, 10.1109/ICIP.1996.560873, 0-7803-3259-8, (437-440) This Issue15 October 1996Volume 354Issue 1716 Article InformationDOI:https://doi.org/10.1098/rsta.1996.0098Published by:Royal SocietyPrint ISSN:1364-503XOnline ISSN:1471-2962History: Manuscript received08/06/1994Manuscript accepted22/01/1996Published online01/01/1997Published in print15/10/1996 License:Scanned images copyright © 2017, Royal Society Citations and impact