1. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis1 Tone Mapping Presented by Lok Hwa

2. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis2 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/m 2

3. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis3 Overview Typical input is global illumination image or HDR camera input Goal is to compress the dynamic range of the input image and reproduce a realistic rendering based on human perception

4. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis4 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)

5. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis5 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)

6. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis6 Why? Why? Why?

7. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis7 Overview – simple methods

8. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis8 Many Operators

9. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis9 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 L 0 = minimum luminance visible alpha = [.333, 0.49] Not valid for complex scenes; chosen for computational simplicity

10. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis10 Spatially Uniform Operators =Luminance of real-world scene target =Luminance of real-world surrounding light and functions of adaptation level

11. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis11 Spatially Uniform Operators Similarly for the display: Matching screen and real-world brightness: For gamma value from 2.2 to 2.5:

12. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis12 Spatially Uniform Operators Full operator: Computing the frame buffer value to produce the desired luminance [0,1]:

13. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis13 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

14. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis14 Tumblin-Rushmeier / Ward

15. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis15 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: L a = adaptation luminance

16. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis16 Spatially Uniform Operators L d = display luminance at an image point L w = world luminance “ Minimum discernible luminance change at L a (d) L a (d) = display adaptation luminance L a (w)= world adaptation luminance Differences just visible in the world will be just visible on the display.

17. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis17 Spatially Uniform Operators Getting from world luminance to display input:

18. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis18 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

19. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis19 Automatic Exposure Determined by taking average luminance and computing a scalefactor that maps it to half the maximum luminance.

20. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis20 Tumblin-Rushmeier / Ward

21. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis21 Ward’s contrast-based scalefactor

22. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis22 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

23. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis23 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).

24. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis24 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.

25. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis25 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

26. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis26 Spatially Varying Operators Log Average: Scale Luminances to a key: a is called the “key value”

27. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis27 Spatially Varying Operators

28. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis28 Spatially Varying Operators Compress the high luminances: Burning high luminances in a controlled fashion:

29. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis29 Spatially Varying Operators

30. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis30 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:

31. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis31 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) roughly in a disc of radius s. V 2 operates on a slightly larger area but same scale

32. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis32 Spatially Varying Operators

33. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis33 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 V 1

34. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis34 Spatially Varying Operators

35. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis35 Comparisons on 12 zone scene

36. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis36 Nutrition Facts

37. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis37

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40. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis40 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.

41. visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis41 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