CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2013 vol.35 Issue No.01 - Jan.
Issue No.01 - Jan. (2013 vol.35)
Iván Marín-Franch , Sch. of Optometry, Indiana Univ., Bloomington, IN, USA
D. H. Foster , Sch. of Electr. & Electron. Eng., Univ. of Manchester, Manchester, UK
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.78
The colors present in an image of a scene provide information about its constituent elements. But the amount of information depends on the imaging conditions and on how information is calculated. This work had two aims. The first was to derive explicitly estimators of the information available and the information retrieved from the color values at each point in images of a scene under different illuminations. The second was to apply these estimators to simulations of images obtained with five sets of sensors used in digital cameras and with the cone photoreceptors of the human eye. Estimates were obtained for 50 hyperspectral images of natural scenes under daylight illuminants with correlated color temperatures 4,000, 6,500, and 25,000 K. Depending on the sensor set, the mean estimated information available across images with the largest illumination difference varied from 15.5 to 18.0 bits and the mean estimated information retrieved after optimal linear processing varied from 13.2 to 15.5 bits (each about 85 percent of the corresponding information available). With the best sensor set, 390 percent more points could be identified per scene than with the worst. Capturing scene information from image colors depends crucially on the choice of camera sensors.
Image color analysis, Entropy, Mutual information, Sensors, Random variables, Lighting, Cameras,color constancy, Color vision, color information, digital color cameras, color processing, information theory, natural scenes, kth-nearest-neighbor statistics
Iván Marín-Franch, D. H. Foster, "Estimating Information from Image Colors: An Application to Digital Cameras and Natural Scenes", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 1, pp. 78-91, Jan. 2013, doi:10.1109/TPAMI.2012.78