Tone scale adjustment of digital images
View Patent ↗A method of processing data from a digital image to enhance the neutral tonescale estimates a neutral offset, a neutral gain and a neutral gamma from the input data and uses these estimated values to transform the input image data.
1. A method of processing data from a digital image to enhance the neutral tonescale thereof, the method comprising the steps of;
estimating a neutral offset from the input image data,
estimating a neutral gain from the input image data,
estimating a neutral gamma from the input image data, wherein the estimated neutral offset, neutral gain and neutral gamma values are used in the following equation:
x″ ( i,j )=[ H α ( x′ ( i,j ))·α· x′ ( i,j )] γ
where x′(i,j)=x(i,j)−neutraloffset
α=neutral gain
γ=neutral gamma
H α =image adaptive highlight shaper and
used to transform the input image data.
2. A method according to claim 1 wherein the function H α has at least two sections, a first section where no shaping is applied and a second section which implements the shoulder shaping.
3. A method according to claim 2 wherein the second section takes a Gaussian form.
4. A method according to claim 3 wherein the Gaussian form is image adaptive, dependent on the value α.
5. A method of processing data from a digital image to enhance the neutral tonescale thereof, the method comprising the steps of:
estimating a neutral offset from the input image data,
estimating a neutral gain from the input image data,
estimating a neutral gamma from the input image data, wherein the estimated neutral offset, neutral gain and neutral gamma values are used in the following equation:
x″ ( i,j )=[ H α ( x ′( i,j )) L α ( x ′( i,j ))·α· x ′( i,j )] γ
where x″(i,j)=x(i,j)−neutraloffset
α=neutral gain
γ=neutral gamma
H α =image adaptive highlight shaper
L α =image adaptive lowlight shaper and
x″(i,j) is used to transform the input image data.
6. A method according to claim 5 wherein the function H α has at least two sections, a first section where no shaping is applied and a second section which implements the shoulder shaping.
7. A method according to claim 6 wherein the second section takes a Gaussian form.
8. A method according to claim 7 wherein the Gaussian form is image adaptive, dependent on the value α.
9. A method according to claim 1 wherein the input image data is sub-sampled before the neutral offset, neutral gain and neutral gamma are estimated.
10. A method according to claim 9 wherein the image data is sub-sampled by 4:1 in each dimension.
11. A method according to claim 9 wherein the image data is pre-filtered to reduce the bandwidth appropriately for sub-sampling.
12. A method according to claim 1 wherein the neutral offset is estimated by calculating the averages of a percentage of pixels having code values corresponding to the darkest portion of the image, for each separation of the input image, the neutral offset being a function of these averages.
13. A method according to claim 12 wherein the percentage of pixels used is in the range of 0.01% to 0.5%.
14. A method according to claim 12 wherein the neutral offset is given by the lowest value of the averages scaled by an optimisable black offset correction factor.
15. A method according to claim 14 wherein the offset correction factor lies in the range of 0.5 to 1.0.
16. A method according to claim 1 wherein the neutral gain is estimated by calculating the averages of a percentage of pixels having code values corresponding to the lightest portions of the image, for each separation of the input image, the neutral gain being a function of these averages.
17. A method according to claim 16 wherein the neutral gain is defined by the following function:
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where gcf=gain correction factor maxmean is the highest value of the averages calculated imscal is a scaling value corresponding to the scaling of the image pixel values.
18. A method according to claim 17 wherein the gain correction factor lies in the range of 0.25 and 1.
19. A method according to claim 1 wherein the neutral gamma is estimated using the following equation;
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where gamcf is a gamma correction factor;
meanlum is the average value of n(i,j), a neutral signal scaled to lie in the range 0–1 and calculated from the pixel values from the image separations each transformed according to v′(i,j)=(v(i,j)−neutral offset)·neutral gain; and
aimpopmean corresponds to the estimated tonescale centre of the image population encoded in the relevant metric.
20. A method according to claim 19 wherein the gamma correction factor lies in the range of 0.25 to 1.
21. A method according to claim 19 wherein the neutral gamma is estimated using the average of the image data that corresponds to edge information in the original image.
22. A method according to claim 21 wherein the neutral offset and neutral gain are also estimated using the average of the input image data that corresponds to edge information of the original image.
23. A method according to claim 1 wherein pixel values in the centre of the image are given a higher statistical weighting than pixel values at the edge of the image.
24. A method according to claim 5 wherein pixel values in the centre of the image are given a higher statistical weighting than pixel values at the edge of the image.
25. A method according to claim 1 wherein pixel values are weighted by scene content.
26. A method according to claim 5 wherein pixel values are weighted by scene content.
27. A method according to claim 1 wherein red, green and blue components of the input image are used.
28. A method of processing data from a digital image to enhance the neutral tonescale thereof, the method comprising the steps of;
estimating a neutral offset from input image data,
estimating a neutral gain from the input image data,
estimating a neutral gamma from the input image data,
using the estimated neutral offset, neutral gain and neutral gamma values to calculate a shaper LUT, the shaper LUT being a function of the estimated values,
creating a tonescale transformation LUT from the estimated values and the shaper LUT; and
using the tonescale transformation LUT to transform the input image data.
29. A method according to claim 28 applied to images encoded in a non logarithmic space.
30. A method according to claim 28 wherein the estimated values are used in the following equation:
x″ ( i,j )=[α ·x′ ( i,j )] γ
where x′(i,j)=x(i,j)−neutraloffset
α=neutral gain
γ=neutral gamma.
31. A method according to claim 28 wherein the estimated values are used in the following equation:
x″ ( i,j )= [H α ( x′ ( i,j ))·α· x′ ( i,j )] γ
where x′(i,j)=x(i,j)−neutraloffset
α=neutral gain
γ=neutral gamma
H α =image adaptive highlight shaper.
32. A method according to claim 31 wherein the function H α has at least two sections, a first section where no shaping is applied and a second section which implements the shoulder shaping.
33. A method according to claim 32 wherein the second section takes a Gaussian form.
34. A method according to claim 33 wherein the Gaussian form is image adaptive, dependent on the value α.
35. A method according to claim 28 wherein the estimated values are used in the following equation:
x″ ( i,j )=[ H α ( x′ ( i,j )) L α ( x′ ( i,j ))·α· x′ ( i,j )] γ
where x′(i,j)=x(i,j)−neutraloffset
α=neutral gain
γ=neutral gamma
H α =image adaptive highlight shaper
L α =image adaptive lowlight shaper.
36. A method according to claim 35 wherein the function H α has at least two sections, a first section where no shaping is applied and a second section which implements the shoulder shaping.
37. A method according to claim 36 wherein the second section takes a Gaussian form.
38. A method according to claim 37 wherein the Gaussian form is image adaptive, dependent on the value α.
39. A method according to claim 28 wherein the input image data is sub-sampled before the neutral offset, neutral gain and neutral gamma are estimated.
40. A method according to claim 39 wherein the image data is sub-sampled by 4:1 in each dimension.
41. A method according to claim 39 wherein the image data is pre-filtered to reduce the bandwidth appropriately for sub-sampling.
42. A method according to claim 28 wherein the neutral offset is estimated by calculating the averages of a percentage of pixels having code values corresponding to the darkest portion of the image, for each separation of the input image, the neutral offset being a function of these averages.
43. A method according to claim 42 wherein the percentage of pixels used is in the range of 0.01% to 0.5%.
44. A method according to claim 42 wherein the neutral offset is given by the lowest value of the averages scaled by an optimisable black offset correction factor.
45. A method according to claim 44 wherein the offset correction factor lies in the range of 0.5 to 1.0.
46. A method according to claim 28 wherein the neutral gain is estimated by calculating the averages of a percentage of pixels having code values corresponding to the lightest portions of the image, for each separation of the input image, the neutral gain being a function of these averages.
47. A method according to claim 46 wherein the neutral gain is defined by the following function:
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where gcf=gain correction factor maxmean is the highest value of the averages calculated imscal is a scaling value corresponding to the scaling of the image pixel values.
48. A method according to claim 47 wherein the gain correction factor lies in the range of 0.25 and 1.
49. A method according to claim 28 wherein the neutral gamma is estimated using the following equation;
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log
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where gamcf is a gamma correction factor;
meanlum is the average value of n(i,j), a neutral signal scaled to lie in the range 0–1 and calculated from the pixel values from the image separations each transformed according to v′(i,j)=(v(i,j)−neutral offset)·neutral gain; and
aimpopmean corresponds to the estimated tonescale centre of the image population encoded in the relevant metric.
50. A method according to claim 49 wherein the gamma correction factor lies in the range of 0.25 to 1.
51. A method according to claim 49 wherein the neutral gamma is estimated using the average of the image data that corresponds to edge information in the original image.
52. A method according to claim 51 wherein the neutral offset and neutral gain are also estimated using the average of the input image data that corresponds to edge information of the original image.
53. A method according to claim 30 wherein pixel values in the centre of the image are given a higher statistical weighting than pixel values at the edge of the image.
54. A method according to claim 31 wherein pixel values in the centre of the image are given a higher statistical weighting than pixel values at the edge of the image.
55. A method according to claim 35 wherein pixel values in the centre of the image are given a higher statistical weighting than pixel values at the edge of the image.
56. A method according to claim 30 wherein pixel values are weighted by scene content.
57. A method according to claim 31 wherein pixel values are weighted by scene content.
58. A method according to claim 35 wherein pixel values are weighted by scene content.
59. A method according to claim 28 wherein red, green and blue components of the input image are used.
60. A method of processing data from a digital image to enhance the neutral tonescale thereof, the method comprising the steps of
estimating a neutral offset from the input image data,
estimating a neutral gain from the input image data,
estimating a neutral gamma from the input image data,
calculating a shaper LUT using the estimated neutral offset, neutral gain and neutral gamma values,
creating a tonescale transformation LUT from the estimated neutral offset, neutral gain and neutral gamma values and the shaper LUT,
calculating the first differential of the tonescale transformation LUT to produce a second LUT,
performing a neutral and color difference rotation on the input image data,
using the tonescale transformation LUT to perform a neutral transformation of the rotated input image data,
using the second LUT to perform an adaptive saturation transformation of the rotated input image data, and
rotating the transformed input data to provide the output image data.
61. A method according to claim 60 applied to images encoded in a non logarithmic space.
62. A method as claimed in claim 60 wherein the neutral transformation used takes the form of the following equation;
neu″ ( i,j )= F ( neu′ ( i,j ))=[ H α ( neu′ ( i,j )) L α ( neu′ ( i,j ))·α· neu′ ( i,j )] γ
where neu′(i,j)=neu(i,j)−neutraloffset.
63. A method as claimed in claim 60 wherein the adaptive saturation transformation takes the form of the following equation;
cr′ ( i,j )= cr ( i,j )[1+({ dF ( neu′ )/ dneu ′}−1)· stf]
for both cr 1 and cr 2 , where stf is the saturation transform factor.
64. A method according to claim 63 wherein the saturation transform factor is in the range of 0.5 to 1.
65. A method according to claim 60 wherein the input image data is rotated in accordance with the following equation.
66. A method according to claim 60 wherein neu, cr 1 and cr 2 are low pass filtered prior to being transformed, and complementary high pass filtered values of neu, cr 1 and cr 2 added to the transformed low pass filtered separations.
67. A method according to claim 60 wherein the input image data is subsampled before the neutral offset, neutral gain and neutral gamma are estimated.
68. A method according to claim 67 wherein the image data is subsampled by 4:1 in each dimension.
69. A method according to claim 60 wherein the neutral offset, neutral gain and neutral gamma are estimated using high contrast edge information in the input image.
70. A method according to claim 60 wherein only neutral components of the input image are used.
71. A method of processing digital images wherein at least part of the method of processing data from the image is a method according to claim 1 .
72. A method of processing digital images wherein at least part of the method of processing data from the image is a method according to claim 28 .
73. A method of processing digital images wherein at least part of the method of processing data from the image is a method according to claim 60 .
74. A computer program product for enhancing the neutral tonescale of a digital image comprising a computer readable storage medium having a computer program stored thereon for performing the steps of
estimating a neutral offset from the input image data,
estimating a neutral gain from the input image data,
estimating a neutral gamma from the input image data; and
using the estimated neutral offset, neutral gain and neutral gamma values in the following equation:
x″ ( i,j )=[ H α ( x′ ( i,j ))·α ·x′ ( i,j )] γ
where x′(i,j)=x(i,j)−neutraloffset
α=neutral gain
γ=neutral gamma
H α =image adaptive highlight shaper and
x″ (i,j) is used to transform the input image data.
75. A computer program product for enhancing the neutral tonescale of a digital image comprising a computer readable storage medium having a computer program stored thereon for performing the steps of estimating a neutral offset from the input image data,
estimating a neutral gain from the input image data,
estimating a neutral gamma from the input image data,
using the estimated neutral offset, neutral gain and neutral gamma values to calculate a shaper LUT, the shaper LUT being a function of the estimated values,
creating a tonescale transformation LUT from the estimated values and the shaper LUT; and
using the tonescale transformation LUT to transform the input image data.
76. A computer program product for enhancing the neutral tonescale of a digital image comprising a computer readable storage medium having a computer program stored thereon for performing the steps of estimating a neutral offset from the input image data,
estimating a neutral gain from the input image data,
estimating a neutral gamma from the input image data,
calculating a shaper LUT using the estimated neutral offset, neutral gain and neutral gamma values,
creating a tonescale transformation LUT from the estimated neutral offset, neutral gain and neutral gamma values and the shaper LUT,
calculating the first differential of the tonescale transformation LUT to produce a second LUT,
performing a neutral and color difference rotation on the input image data,
using the tonescale transformation LUT to perform a neutral transformation of the rotated input image data,
using the second LUT to perform an adaptive saturation transformation of the rotated input image data; and
rotating the transformed input data to provide the output image data.
77. A device for processing data from a digital image to enhance the neutral tonescale thereof, the apparatus including;
means for estimating a neutral offset from input image data,
means for estimating a neutral gain from the input image data,
means for estimating a neutral gamma from the input image data; and
means for using the estimated neutral offset, neutral gain and neutral gamma values in the following equation
x ″( i,j )=[ H α ( x′ ( i,j ))·α· x′ ( i,j )] γ
where x′(i,j)=x(i,j)−neutraloffset
α=neutral gain
γ=neutral gamma
H α =image adaptive highlight shaper and
x″(i,j) is used to transform the input image data.
78. A device for processing data from a digital image to enhance the neutral tonescale thereof, the apparatus including;
means for estimating a neutral offset from input image data,
means for estimating a neutral gain from the input image data,
means for estimating a neutral gamma from the input image data,
means for using the estimated neutral offset, neutral gain and neutral gamma values to calculate a shaper LUT, the shaper LUT being a function of the estimated values,
means for creating a tonescale transformation LUT from the estimated values and the shaper LUT; and
means for using the tonescale transformation LUT to transform the input image data.
79. A device for processing data from a digital image to enhance the neutral tonescale thereof, the apparatus including;
means for estimating a neutral offset from the input image data,
means for estimating a neutral gain from the input image data,
means for estimating a neutral gamma from the input image data,
means for calculating a shaper LUT using the estimated neutral offset, neutral gain and neutral gamma values,
means for creating a tonescale transformation LUT from the estimated neutral offset, neutral gain and neutral gamma values and the shaper LUT,
means for calculating the first differential of the tonescale transformation LUT to produce a second LUT,
means for performing a neutral and color difference rotation on the input image data,
means for using the tonescale transformation LUT to perform a neutral transformation of the rotated input image data,
means for using the second LUT to perform an adaptive saturation transformation of the rotated input image data, and
means for rotating the transformed input data to provide the output image data.
80. A system for the processing of digital image data wherein a digital image is input, the image is processed and the processed image stored and/or output, at least part of the processing of the image comprising a method according to claim 1 .
81. A system for the processing of digital image data wherein a digital image is input, the image is processed and the processed image stored and/or output, at least part of the processing of the image comprising a method according to claim 28 .
82. A system for the processing of digital image data wherein a digital image is input, the image is processed and the processed image stored and/or output, at least part of the processing of the image comprising a method according to claim 60 .