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Image Synthesis

Image Synthesis. HDRI. Problem. The world and our visual system has a HDR… … but not our digital equipment. Need to find ways to. Record Store Process HDR Data Convert and output. Recording. Engineering versus Nature.

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Image Synthesis

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  1. Image Synthesis HDRI

  2. Problem The world and our visual system has a HDR… … but not our digital equipment

  3. Need to find ways to • Record • Store • Process HDR Data • Convertand output

  4. Recording Engineering versus Nature

  5. Lense-System

  6. Chromatic aberration Human Engineer

  7. Chromatic aberration Human Engineer

  8. Chromatic aberration

  9. Resolution Retina CCD - Charge-coupled Device

  10. Resolution Estimated overall resolution:15 to 576 megapixels

  11. Resolution

  12. Dynamic Range From: 100:1 (single focus) to 1,000,000:1 (Purkinjeeffect) 16384:1

  13. Nature the clear looser? Not really  excessive post processing

  14. Post processing

  15. Post processing

  16. Post processing

  17. Post processing… …is executed parallel and hierarchical

  18. Alternatives HDR Cameras Multiple Exposures expensive dynamic scenes

  19. Merging multiple exposures • camera exposure curve • rescale and addweighted mean in overlap regions

  20. Storage, first a little bit about colors

  21. …a little bit about colors

  22. Color Encoding • Primary colorsRGB, CMYK, XYZwith transformation formula (often a matrix) • color gamut • quantization

  23. Encoding Schemes • Pixar Log Encoding (TIFF)11 bit log/exponential encoding • Radiance RGBE Encoding (HDR)8bit RGB mantissa, 8bit shared exponent • SGI LogLuv (TIFF)16bit logarithmic luminance+ 2 * 8bit u,vcoords • ILM OpenEXR (EXR)16bit floating point format (half)

  24. Processing • load data • convert to 3 * 32bit float / 3 * 16bit float • compute whatever you want • encode down into preferred file formatand store

  25. Output Solution 1: Get a HDR Screen (BRIGHTSIDE)

  26. Output Solution 2: Tone Mapping

  27. Image arithmetic Applystandardarithmeticorlogicaloperationstoimages (pixelvalues) • Output pixelonlydepends on correspondingpixels in inputimages • Images must havethe same size • Veryefficient in termsofperformanceandmemoryrequirements • No intermediate resultneeded

  28. Image arithmetic Operators • Addition, Subtraction • Overlay, backgroundremoval, noisereduction • Multiplication (Scaling) • Brighten, darken • Blending/compositing (weightedaverage) • Multi-modalityfusion • Logical bitwiseoperations • AND/NAND (Intersection) • OR/NOR (Union) • Invert/Logical NOT

  29. Image arithmetic Examples Intensity scaling A & B !A & !B A B Detect object that did not move

  30. Image arithmetic • Example • Outline edges in the original image + Wrap around of pixel values!

  31. Image arithmetic • Example (cont.) • Generate mask by thresholding and AND the inverted mask with the image

  32. Image arithmetic • Example (cont.) • Add unthresholded mask to original image

  33. Image arithmetic • Example • Blending X • P0 + (1-X) • P1 • Reduces contrast before adding X=0.7 X=0.5

  34. Point operators • Thresholding • Cut arbitraryranges • Normalization (contraststretching) • Sout = (Sin – Min) / (Max – Min) • Logarithmic/exponentialoperators • Enhance/supresslowintensitypixels • Histogramequalization • monotonic, non-linear modificationofthedynamicintensityrange Histogram Histogramshowsthedistributionofpixelsamongintensityvalues

  35. Point operators • Histogram equalization • Re-assign intensities • Output image has uniform intensity distribution • Cannot be achieved in general • Find transfer function that maps input values a to output values b such as to approximate a flat histogram • Relative order (in terms of intensity) of pixels will not be destroyed

  36. Point operators • Consider probability density function p(x) and probability distribution function P(x) • P(a) is the probability that a brightness chosen from a region is less than or equal to a given brightness value a • P(a) =x=0a p(x) • The probability that a brightness in a region falls between a and a+a is p(a)a = dP(a)/daa • Brightness probability density function is given by the histogram

  37. Point operators • Mapping F (ab) forhistogramequalization • pa(a)da = pb(b)db dF=db=pa(a)da/pb(b) • Becausepb(b) shouldbeconstantto (1/(2B-1)): F(a)=(2B-1)P(a) • Digital implementation: f(a)=max(0,round(2B-1 • na/N)-1) na: numberofpixelswithintensity a N: Numberofpixels

  38. Point operators Beforeand after histogramequalization

  39. Point operators Beforeand after histogramequalization

  40. Point operators Beforeand after histogramequalization

  41. Point operators • Before and after histogram equalization

  42. Tone Mapping • Visibility is reproducedyou can see an object on the display iff you can see it in the real scene • Viewing the image produces the same subjective experience as viewing the real scene

  43. Tone Mapping

  44. Tone Mapping

  45. Tone Mapping • Setting the brightest pixel to 1 scaling the rest linearly • Light sources are multiple orders of magnitude brighter Scene is almost entirely black • Setting the brightest non light-source pixel to 1-e scaling the rest linearly/logarithmic • Light sources are still mapped to 1 • Visibility is preserved • but experience is not preserved • changes in light emission have no effect on the image

  46. Tone Mapping [Ward 97] Compute Luminance Histogram

  47. Tone Mapping Compute Histogram Equalization

  48. Tone Mapping Apply Linear Ceiling

  49. Tone Mapping Apply just noticeable difference ceiling

  50. Tone Mapping Apply veiling luminance effects (blooming)

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