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Tone Mapping

Tone Mapping. Presented by Lok Hwa. Overview. Tone mapping/reproduction : mapping the potentially high dynamic range (HDR) of real world luminance to the low dynamic range of devices with limited range of luminance

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Tone Mapping

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  1. Tone Mapping Presented by Lok Hwa Realistic Image Synthesis

  2. Overview • Tone mapping/reproduction:mapping the potentially high dynamic range (HDR) of real world luminance to the low dynamic range of devices with limited range of luminance • The "dynamic range" of a scene is the contrast ratio between its brightest and darkest parts. • 100:1 vs. 100,000,000:1 cd/m2 Realistic Image Synthesis

  3. Overview • Typical input is global illumination image or HDR camera input • Goal is to compress the dynamic range of the input imageand reproduce a realistic rendering based on human perception Realistic Image Synthesis

  4. Two main approaches: Global: single-scale; spatially uniform; computes final image using one function for all pixels Local: multi-scale; spatially varying; compute final image using different functions for every pixel. • global is usually faster but local is usually better in quality • static vs. dynamic dynamic: time-dependent; accounts for observers adaptation to background luminance (light and dark adaptation) Realistic Image Synthesis

  5. Overview • Problems: -many require human adjustment -visual artifacts (ringing or visible clamping) -not robust -lack of validation -more complex than simply matching brightness/contrast (e.g. visual acuity) Realistic Image Synthesis

  6. Why? Why? Why? Realistic Image Synthesis

  7. Overview – simple methods Realistic Image Synthesis

  8. Many Operators Realistic Image Synthesis

  9. Spatially Uniform Operators Tumblin and Rushmeier 1991, 1993 • Focused on preserving overall brightness • Based on Stevens and Stevens power-law in Journal of the Optical Society of America. • Subjective brightness, B • k = constant • L0 = minimum luminance visible • alpha = [.333, 0.49] • Not valid for complex scenes; chosen for computational simplicity Realistic Image Synthesis

  10. Spatially Uniform Operators =Luminance of real-world scene target =Luminance of real-world surrounding light and functions of adaptation level Realistic Image Synthesis

  11. Spatially Uniform Operators Similarly for the display: Matching screen and real-world brightness: For gamma value from 2.2 to 2.5: Realistic Image Synthesis

  12. Spatially Uniform Operators Computing the frame buffer value to produce the desired luminance [0,1]: Full operator: Realistic Image Synthesis

  13. Spatially Uniform Operators • Cons: • Limited to grayscale • Preserves brightness, but loses detail in HDR scenes • Can handle extreme brightness, but image tend to be darker Realistic Image Synthesis

  14. Tumblin-Rushmeier / Ward Realistic Image Synthesis

  15. Spatially Uniform Operators • Greg Ward 1994 "A contrast-based scalefactor for Luminance Display" • Concentrated on preserving contrast • Linear function (scalefactor) instead w/ potential advantage: Darker scene creates a darker display which may be more natural than a display with a similar mean, but reduced contrast • Based on 70's Blackwell data. Flashed a dot on the screen with a background to test visual response. Minimum visible luminance difference at the display adaptation level: La = adaptation luminance Realistic Image Synthesis

  16. Spatially Uniform Operators Ld = display luminance at an image point Lw = world luminance “ Minimum discernible luminance change at La(d) La(d) = display adaptation luminance La(w)= world adaptation luminance Differences just visible in the world will be just visible on the display. Realistic Image Synthesis

  17. Spatially Uniform Operators Getting from world luminance to display input: Realistic Image Synthesis

  18. Spatially Uniform Operators • World adaptation level can be determined by log average of image excluding light sources not in direct line of sight (global) or use a local area of an image. • In dark scene, the final image is darker. • More simpler viewer model. Cons -detail is still lost in areas which values must be clamped Realistic Image Synthesis

  19. Automatic Exposure Determined by taking average luminance and computing a scalefactor that maps it to half the maximum luminance. Realistic Image Synthesis

  20. Tumblin-Rushmeier / Ward Realistic Image Synthesis

  21. Ward’s contrast-based scalefactor Realistic Image Synthesis

  22. Spatially Varying Operators • Reinhard et al. 2002 "Photographic Tone Reproduction for Digital Images • Photographers have faced this same problem for many years • Technique is based on famous photographer Ansel Adams studies on tone reproduction • using the Zone System (his invention); still widely used and practical Realistic Image Synthesis

  23. Spatially Varying Operators Definitions: • Zone: 11 print zones related logarithmically to scene luminance. • Dynamic Range for Photographers: We can use the zones to calc the difference between highest and lowest scene zones (photographic dynamic range) • Key: Subjective measure of light (high key) or dark (low key). • Dodging and Burning: Print technique where more light is exposed to a region to dodge or withhold light from that area or burn (darken). Realistic Image Synthesis

  24. Spatially Varying Operators • The Zone System starts by a photographer taking a luminance reading of a surface they think is middle-grey (subjective middle brightness in scene, typically zone 5). • Middle grey is also usually mapped to 18% reflectance of the print. • They take luminance readings of the light and dark regions to obtain a dynamic range. • A range within 9 zones ensures all detail can be captured. Otherwise certain areas we be clamped to pure white or black. These areas can be dodged or burned to change the local detail of a region. Realistic Image Synthesis

  25. Spatially Varying Operators Algorithm: • Use the log-average luminance to find the "key" of a scene • Automatic dodging a burning (as in photography): all portions of the print receive difference exposure time Realistic Image Synthesis

  26. Spatially Varying Operators • Log Average: • Scale Luminances to a key: a is called the “key value” Realistic Image Synthesis

  27. Spatially Varying Operators Realistic Image Synthesis

  28. Spatially Varying Operators • Compress the high luminances: • Burning high luminances in a controlled fashion: Realistic Image Synthesis

  29. Spatially Varying Operators Realistic Image Synthesis

  30. Spatially Varying Operators Dodging and Burning • Typically applied to regions bounded by large contrasts • The size of a local region is estimated using a measure of local contrast; computed at multiple spatial scales • At each spatial scale, a center-surround function is implemented by subtracting two Gaussian blurred images. • Gaussian profiles are of the form: Realistic Image Synthesis

  31. Spatially Varying Operators • Response function of image location, scale, and luminance distribution L: • Center-surround function: • a = key value, phi is the sharpening parameter • Provides a local average of the luminance around (x,y) roughlyin a disc of radius s. • V2 operates on a slightly larger area but same scale Realistic Image Synthesis

  32. Spatially Varying Operators Realistic Image Synthesis

  33. Spatially Varying Operators • To choose the largest neighborhood around a pixel with fairly even luminances: (start from the lowest scale and stop when this is satisfied) • The global operator is converted to a local operator by replacing L with V1 Realistic Image Synthesis

  34. Spatially Varying Operators Realistic Image Synthesis

  35. Comparisons on 12 zone scene Realistic Image Synthesis

  36. Nutrition Facts Realistic Image Synthesis

  37. Realistic Image Synthesis

  38. Realistic Image Synthesis

  39. Realistic Image Synthesis

  40. Conclusion • In general there is a need for validation of tone operators. Mostly subjective. • Availability of HDR monitors display a must wider range of luminosity in which most scenes can be viewed accurately. This makes direct comparison available between two monitors and better validation. Realistic Image Synthesis

  41. Resources • Devlin, Chalmers, Wilkie, Purgathofer. “Tone Reproduction and Physically Based Spectral Rendering” • Reinhard, Stark, Shirley, Ferwerda. “Photographic Tone Reproduction for Digital Images” • Greg Ward. “A contrast-based scalefactor for luminance display” • Ward, Piatko. “A Visibility Matching Tone Reproduction Operator for High Dynamic Range Scenes” • http://graphics.cs.uni-sb.de/Courses/ss00/pbs/Tonemapping-Dateien/frame.htm • http://www.devebec.org • http://www.cgg.cvut.cz/~cadikm/tmo/ • http://aris-ist.intranet.gr/documents/Tone%20Mapping%20and%20High%20Dinamic%20Range%20Imaging.pdf Realistic Image Synthesis

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