1 / 74

Half Toning

Half Toning. Continuous Half Toning. Color Half Toning. Half toning and Colors. Digital Half Toning. Half Toning. Emulating 5 different levels. Half Toning. 10 levels. Original. Half Toning. Original. Dithering. Dithering and Halftoning. Trade spatial for intensity resolution

dphillip
Download Presentation

Half Toning

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Half Toning

  2. Continuous Half Toning

  3. Color Half Toning

  4. Half toning and Colors

  5. Digital Half Toning

  6. Half Toning Emulating 5 different levels

  7. Half Toning 10 levels

  8. Original

  9. Half Toning

  10. Original

  11. Dithering

  12. Dithering and Halftoning Trade spatial for intensity resolution (works well for printing where dot printing is very high) • Thresholding. • Random dither; Robert’s algorithm • Ordered dither • Error diffusion Your eye will average over an area - Spatial Integration

  13. Thresholding Assume we want to quantize a gray-level image to a binary colormap. Map the upper half of the gray-level scale to white, and the lower half to black – a simple threshold operation, preformed independently at each pixel.

  14. Thresholding Simple threshold. Original image. n = 0.5 Errors are low spatial frequencies.

  15. Robert’s Algorithm • First add noise • Then quantize i r + 1 1 Quantized to 1 Quantized to 0 r 0 x Moves errors to higher spatial frequencies. -> eye averages over an area.

  16. Threshold

  17. Threshold + Noise

  18. Robert’s Algorithm Pink Blue

  19. The trouble with noise • Difficult to compute quickly. • Not reproducible. • Pre-compute pseudo-random function and store in table. • Small tiled patterns sufficient

  20. Dithering • It is possible to improve the quality of a quantized image by distributing the quantized error. • Let’s have a closer look.

  21. Dithering Thresholding Dithering

  22. Dithering Each pixel produces a quatization error The quality of the result may be improved by adjusting the threshold locally, so that adjacent pixels in small areas are quantized with different thresholds. This reduces the average local quantization error. Matrices of these threshold are called dither matrices.

  23. Threshold + Noise

  24. Dithering

  25. Ordered Dithering • Trade off spatial resolution for intensity resolution. • Use dither patterns. • Can be represented as a matrix.

  26. Other possibilities

  27. 3 7 5 6 1 2 9 4 8 The dithering matrix (3x3) For all Xpixels For all Ypixels v = approximate(x,y) i = x mod 3 j = y mod 3 if v >= M[i,j] then Set_Pixel(x,y, BLACK) else Set_Pixel(x,y, WHITE)

  28. 0 3 0 7 0 5 3 7 7 5 5 8 1 6 2 4 5 3 2 6 0 1 1 2 1 6 6 1 2 8 7 2 2 5 4 3 2 3 9 0 1 4 8 0 4 9 9 4 4 8 8 3 7 5 2 6 4 3 3 7 5 8 9 7 7 2 2 1 3 2 6 6 1 2 2 9 9 7 4 3 2 2 4 8 9 9 4 4 8 8 8 4 4 8 4 4 Dithering 3 7 5 6 1 2 Dithering mask 9 4 8 2 1 3 Image 3 2 2 4 8 4 Binary image

  29. Original

  30. Dithering

  31. Dithering

  32. Error Diffusion

  33. Floyd-Steinberg Error Diffusion With this method, the average quatization error is reduced by propagating the error from each pixel to some of its neighbors in the scan order.

  34. 1D Error Diffusion 1 0 1

  35. 1D Error Diffusion 1 0

  36. 1D Error Diffusion 1 0

  37. 1D Error Diffusion 1 0

  38. 1D Error Diffusion 1 0 1 0

  39. 1D Error Diffusion 1 0 1 0 1

  40. 1D Error Diffusion 1 0

  41. 1D Error Diffusion 1 0

  42. 1D Error Diffusion 1 0

  43. e e -3e/8 -3e/8 -e/4 -3e/8 -e/4 -3e/8 Floyd-Steinberg Error Diffusion With this method, the average quatization error is reduced by propagating the error from each pixel to some of its neighbors in the scan order. Note that the error propagation weights must sum to one

  44. Dither vs. Floyd-Steinberg

  45. Original Picture

  46. Error diffusion result Dithering result

  47. Examples – Continue

  48. Dithering Dithering: Note that each square ring is of different brightness

  49. Error Diffusion Error Diffusion: Note that the error is distributed across the layers

More Related