Spatiochromatic Vision Models for Imaging Jan P Allebach
Spatiochromatic Vision Models for Imaging Jan P. Allebach School of Electrical and Computer Engineering Purdue University West Lafayette, Indiana allebach@purdue. edu CIC-15, Albuquerque, CIC-17, Albuquerque NM, 6 November 10 November 2007 2009
What is a model? From dictionary. com: A schematic description of a system, theory, or phenomenon that accounts for its known or inferred properties and may be used for further study of its characteristics. • Model is not a complete description of the phenomenon being modeled. • It should capture only what is important to the application at hand, and nothing more. • Its structure must be responsive to resource constraints. Advanced Digital Halftoning 17 -19 Oct. 2016
Visual system components Advanced Digital Halftoning 17 -19 Oct. 2016
Why do we need spatiochromatic models? • Imaging systems succeed by providing a facsimile of the real world • A few primaries instead of an exact spectral match • Spatially discretized and amplitude quantized representation of images that are continuous in both space and amplitude • These methods only succeed only because of the limitations of the human visual system (HVS) • To design lowest cost systems that achieve the desired objective, it is necessary to take into account the human visual system in the design and evaluation Advanced Digital Halftoning 17 -19 Oct. 2016
Modeling context • Modeling process is very dependent on the intended application - Motivation for developing the models in the first place - Governs choice of features to be captured and computational structure of the model - Provides the final test of the success of the model • Tight interplay between models for imaging system components and the human visual system • Model usage may be either embedded or external Advanced Digital Halftoning 17 -19 Oct. 2016
Pedagogical approach • Spatiochromatic modeling, in principle, builds on all of the following areas: - Color science - Imaging science - Psychophysics - Image systems engineering • As stated in course description, we assume only a rudimentary knowledge of these subjects • Start from basic principles, but move quickly to more advanced level • Focus on what is needed to follow the modeling discussion Advanced Digital Halftoning 17 -19 Oct. 2016
The retinal image is what counts • Every spatiochromatic model has an implied viewing distance • What happens when this condition is not met? - Too far – image looks better than specification - Too close – may see artifacts Advanced Digital Halftoning 17 -19 Oct. 2016
Basic spatiochromatic model structure Advanced Digital Halftoning 17 -19 Oct. 2016
Impact of viewing geometry on spatial frequencies • Both arrows A and B generate same retinal image • For small ratio , the angle subtended at the retina in radians is Advanced Digital Halftoning 17 -19 Oct. 2016
Spatial frequency conversion • To convert between (cycles/inch) viewed at distance (inches) and (cycles/degree) subtended at the retina, we thus have • For a viewing distance of 12 inches, this becomes Advanced Digital Halftoning 17 -19 Oct. 2016
Spatial frequency filtering stage • Based on pyschophysical measurements of contrast sensitivity function • Use sinusoidal stimuli with modulation along achromatic, red-green, or blue-yellow axes • For any fixed spatial frequency, threshold of visibility is depends only on. This is Weber’s Law. Advanced Digital Halftoning 17 -19 Oct. 2016
Campbell’s contrast sensivity function on log-log axes Advanced Digital Halftoning 17 -19 Oct. 2016
Dependence of sine wave visibility on contrast and spatial frequency Advanced Digital Halftoning 17 -19 Oct. 2016
Models for achromatic spatial contrast sensitivty* *Kim and Allebach, IEEE T-IP, March 2002 Author Contrast sensitivity function Campbell 1969 Mannos 1974 Nasanen 1984 Daly 1987 Advanced Digital Halftoning 17 -19 Oct. 2016 Constants
Achromatic spatial contrast sensitivity curves Advanced Digital Halftoning 17 -19 Oct. 2016
Chrominance spatial frequency response • Based on Mullen’s data* *K. T. Mullen, J. Physiol. , 1985 Advanced Digital Halftoning 17 -19 Oct. 2016
Spatial Frequency Response of Opponent Channels Luminance [Nasanen] cyc les/ sam ple e ampl cles/s cy Chrominance [Kolpatzik and Bouman*] cyc les/ sam ple *B. Kolpatzik, and C. A. Bouman, J. Electr. Imaging, July 1992 Advanced Digital Halftoning 17 -19 Oct. 2016 mple s/sa cycle
Illustration of difference in spatial frequency response of luminance and chrominance channels Original image Advanced Digital Halftoning 17 -19 Oct. 2016 O 1 - filtered
Illustration of difference in spatial frequency response of luminance and chrominance channels Original image Advanced Digital Halftoning 17 -19 Oct. 2016 O 2 - filtered
Illustration of difference in spatial frequency response of luminance and chrominance channels Original image Advanced Digital Halftoning 17 -19 Oct. 2016 O 3 - filtered
Application areas for spatiochromatic models • Color image display on low-cost devices - PDA - Cellphone • Color image printing - Inkjet - Laser electrophotographic • Digital video display - LCD - DMD - Plasma panel • Lossy color image compression - JPEG - MPEG Advanced Digital Halftoning 17 -19 Oct. 2016
- Slides: 21