2015/10/22 by Satoshi Oshima, Rica Mochizuki, Rika Mochizuki +2 · 18 citations
Computer Science · Mathematics · Neuroscience · Physics and Astronomy · #Advanced Vision and Imaging #Artificial intelligence #Color Science and Applications #Color difference #Color histogram #Color image #Color normalization #Color space #Computer science #Computer vision #Image (mathematics) #Image processing #Matching (statistics) #Mathematics #Observer (physics) #Pattern recognition (psychology) #Statistics #Visual perception and processing mechanisms #cs.CV
paper · pdf · doi:10.1109/tip.2016.2539679
published in IEEE Transactions on Image Processing 25(6), 2587-2600 (Institute of Electrical and Electronics Engineers) · Full resolution color pictures are available from the authors
arxiv created 2015/10/22 · openalex publication_date 2016/03/08 · arxiv updated 2016/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
We use methods from Riemann geometry to investigate transformations between the color spaces of color-normal and color-weak observers. The two main applications are the simulation of the perception of a color weak observer for a color-normal observer, and the compensation of color images in a way that a color-weak observer has approximately the same perception as a color-normal observer. The metrics in the color spaces of interest are characterized with the help of ellipsoids defined by the just-noticeable-differences between the colors which are measured with the help of color-matching experiments. The constructed mappings are the isometries of Riemann spaces that preserve the perceived color differences for both observers. Among the two approaches to build such an isometry, we introduce normal coordinates in Riemann spaces as a tool to construct a global color-weak compensation map. Compared with the previously used methods, this method is free from approximation errors due to local linearizations, and it avoids the problem of shifting locations of the origin of the local coordinate system. We analyze the variations of the Riemann metrics for different observers obtained from new color-matching experiments and describe three variations of the basic method. The performance of the methods is evaluated with the help of semantic differential tests.