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Meet Icam: a Next-Generation Color Appearance Model

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Meet Icam: a Next-Generation Color Appearance Model Meet iCAM: A Next-Generation Why Are We Here? Color Appearance Model CIC X, 2002 Mark D. Fairchild & Garrett M. Johnson • Spatial, Temporal, & Image Quality Questions Remain RIT Munsell Color Science Laboratory www.cis.rit.edu/mcsl • E.g. Pattanaik et al. 1998 Outline Very Brief History of Color Appearance Models • Very Brief History of Color Appearance Models • You’ve just heard two talks on CIECAM02 • Image Appearance Modeling • iCAM: An Image Appearance Model • Enough said… • Future Directions 1 What Does a Color Appearance Model Enable? Appearance Correlates • Brightness, Lightness • Mapping from Measurements to Words • Colorfulness, Chroma, Saturation (Physics to Perception) • Hue • Prediction of Color Matches (or Changes) across Changes in Viewing Conditions History of Color Appearance Models A Next Generation CAM 1970’s: CIELAB and CIELUV Early 1980’s: Initial Hunt and Nayatani Color Appearance Models Late 1980’s: Revisions of Hunt and Nayatani Models Early 1990’s: Model Testing, Further Revisions, New Models (e.g., RLAB, LLAB) Late 1990’s: Convergence … CIECAM97s Early 2000’s: Widespread focused testing and refinement, CIECAM02 is an Evolution of CIECAM97s CIECAM02, Practical Image Appearance Models New Capabilities Require a New Approach … 2 What is an Image Appearance Model? What are Some of the Missing Links? • Spatial Vision (Filtering & Adaptation) • Image appearance models extend color • Scene Interpretation appearance models to include spatial vision, • Computational Surround Effects temporal vision, and image difference/quality • Color/Image Difference Metrics properties. • Image Processing Efficiencies • They account for more complex changes in visual response in a more automated manner. scene representation spatial sampling, optical image formation simulated retinal image calculation of photoreceptor responses MOM Rod S-cone M-cone L-cone Image Quality Measurement response response response response image image image image rod S M L pyramidal image decomposition lowpass lowpass lowpass lowpass Gaussian Gaussian Gaussian Gaussian pyramid pyramid pyramid pyramid rod S M L upscaling, subtraction Johnson & Fairchild, CIC (2001) bandpass bandpass bandpass bandpass contrast contrast contrast contrast images images images images Pattanaik, Ferwerda, Fairchild, & rod S M L band-limited, local, adaptation processing Visual Encoding adapted adapted adapted adapted contrast contrast contrast contrast signals signals signals signals Greenberg, SIGGRAPH and CIC (1998) rod S M L opponent color space transformation adapted adapted adapted adapted contrast contrast contrast contrast signals signals signals signals rod A C1 C2 Modular Framework for IQ Scales nonlinear contrast transduction, thresholding perceived perceived perceived contrast contrast contrast signals signals signals A C1 C2 Multi-Scale Observer Model inversion of visual encoding for display •Promising Framework for Image (mapped) (mapped) (mapped) bandpass bandpass bandpass contrast contrast contrast images S images M images L image reconstruction Differences Comprehensive (mapped) (mapped) (mapped) S-cone M-cone L-cone response response response image S image M image L color space transformation, gamma correction Display Mapping •Flexible Implementation displayed displayed displayed Extremely Complex image image image R G B Successfully Implemented 2-3 Times!! 3 Image Difference Process Meet iCAM Reproduction 1 Reproduction 2 iCAM — image Color Appearance Model A simple framework for color appearance, spatial vision effects, image difference (quality), image processing, and temporal effects (eventually). * * Mean DE ab 2.5 Mean DE ab 1.25 Mean DIm 0.5 Mean DIm 1.5 Spatial Filtering, Local Attention, Local & Global Contrast, CIE Color Difference Pointwise iCAM Spatial iCAM 4 iCAM Performance Examples Basic Appearance Attributes • Chromatic Adaptation Transform (CAT) • Chromatic Adaptation Transform (CAT) – Identical to CIECAM02 • Color Appearance Scales • Constant Hue Lines • Color Appearance Scales • Simultaneous Contrast – Similar to Munsell / CIECAM02 (limited) • Chroma Crispening • Hue Spreading • Constant Hue Lines • HDR Tone Mapping – Best Available (IPT) – Facilitates Gamut Mapping • Image Difference (Quality) iCAM Simultaneous Contrast iCAM Chroma Crispening Original Image iCAM Lightness Original Image iCAM Chroma http://www.hpl.hp.com/personal/Nathan_Moroney/ 5 iCAM Spreading iCAM High-Dynamic-Range Tone Mapping Earlier-Model Results Original Image iCAM Hue www.debevec.org iCAM Image Difference (Image Quality) iCAM Image Difference (Image Quality) (a) 14 1.2 12 1 10 0.8 8 DIm 0.6 DIm 6 0.4 Model Prediction Model Prediction 4 0.2 2 0 0 -5 -4 -3 -2 -1 0 1 2 -4.00 -3.00 -2.00 -1.00 0.00 1.00 2.00 Perceived Difference Perceived Contrast Image Difference Prediction (Sharpness Data) Image Difference Prediction (Contrast Data) 6 Image Input Data Transform to “Sharpened Cone Responses” XYZadapt: Different Filters for luminance & chromaticity TBD The same 3x3 as adopted for CIECAM02. All Filters: Viewing-Distance Dependent TC8-01 (CIECAM02) Linear CAT Surround & Luminance Dependent Transform to IPT Color Space The same CAT as CIECAM02 for simple conditions. “Contrast” depends on luminance and surround. 7 Rectangular-to-Cylindrical Conversion to Brightness, Colorfulness Lightness, Chroma, Hue Just plain geometry. Dependency on absolute luminance. Spatial iCAM Mathematica Notebooks Detailed references to each step in proceedings. Coded examples on the <www.cis.rit.edu/mcsl/iCAM/> internet. • Can be read and executed with free “MathReader”. Open-Source Science • Other code (Matlab, IDL) forthcoming. • Updates 8 Conclusions Future Directions • iCAM Represents an Example of a New Generation of • HDR Digital Photography (Capture & Processing) Color Appearance Model • Video iCAM (Temporal Adaptation & Filtering) • HDR Digital Video (Processing) • Image Appearance Model • Better Understanding of Surround Effects • Incorporates Spatial Vision • Image-Content Dependent Reproduction • Can Be Extended for Temporal Vision (EI 2003) • Refined Image Difference & Image Quality Metrics • Image Difference Metric, DIm (EI 2003) • Extension to Preferred Image Reproduction • Basis for a Fundamental Image Quality Metric • Psychophysics, Psychophysics, Psychophysics Suggestions and Help Welcome and Encouraged 9.
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