Method and apparatus for the analysis and correction of the image gradation in image originals

A method and apparatus for the analysis and correction of the image gradation of an image original to be reproduced by evaluating image values acquired by point-by-point and line-by-line, optoelectronic scanning with an input device in apparatus and systems for image processing. The image original is geometrically subdivided into a plurality of sub-images. The frequency distribution of the image values or, respectively, of the luminance components of the color values in a corresponding sub-image is separately identified as a sub-image histogram. The sub-image histograms of the individual sub-images are evaluated and the sub-images relevant for the image gradation are identified by means of the evaluation. An aggregate histogram that corresponds to the frequency distribution of the image values or, respectively, of the luminance component of the color values in the relevant sub-images is calculated from the sub-image histograms of the relevant sub-images. Correction values for the correction of the image gradation characteristic of the image original are subsequently calculated from the aggregate histogram according to the method of histogram modification.

BACKGROUND OF THE INVENTION 
The invention generally relates to methods and apparatus for electronic 
reproduction of an image. More specifically, the invention is directed to 
a method and to an apparatus for the analysis and correction of the image 
gradation in image originals in apparatus and systems for electronic image 
processing. As used herein, the term "image originals" refers to and means 
black-and-white originals and color originals. 
As is known, electronic image processing is essentially composed of the 
steps of image input, image processing and image output. Analysis of image 
gradation generally is undertaken during image processing for the 
acquisition of correction curves for the correction of image gradation 
characteristics to effect contrast corrections in image originals. 
In the image input step with, for example, a color image scanner (scanner) 
as an image input device, three primary color value signals R, G, B are 
acquired by trichromatic as well as pixel-by-pixel and line-by-line 
scanning of color originals to be reproduced with an optoelectronic 
scanner element, whereby the individual color value triads represent the 
color components "red" (R), "green" (G) and "blue" (B) of the pixels 
scanned in the color original. The analog color values are converted into 
digital color values and are stored for the subsequent image processing. 
In the image processing step, the colors R, G, B are usually first 
converted into color separation values C, M, Y, K according to the laws of 
subtractive color mixing, these color separation values C, M, Y, K being a 
measure for the dosages of the inks "cyan" (C), "magenta" (M), "yellow" 
(Y) and "black" (K) or, respectively, for the raster point sizes or raster 
percentages employed in the later printing process. 
Over and above this, various image parameters such as light image values 
and dark image values for an adaptation of the image scope, color cast 
values for a color cast correction or a correction curve for a correction 
of over-exposures and under-exposures or for a contrast correction are 
additionally set. Further, local and selective color corrections can also 
be undertaken in color originals, with the goal of improving the image 
reproduction, compensating deficiencies or undertaking editorial changes. 
The setting of the image parameters by an operator usually begins with the 
operator first pre-setting standard values that he determines based on a 
rough pre-classification of the corresponding image original or based on 
an experienced guess. While setting the image parameters, the operator 
makes use of the measuring functions of the image input device, in that he 
measures characteristic picture elements in the image original with the 
optoelectronic scanner element with respect to image scope, color cast and 
luminance distribution and employs the results for finding optimum setting 
values. 
After the image processing step, the image output step is undertaken with a 
suitable image output device, for example, a color separation recorder 
(recorder) or printer for the rastered recording of color separations on a 
film material. 
The interpretation of the measured results and their conversion into 
optimum setting values for the image gradation requires a great deal of 
experience and often presents an inexperienced operator with difficulties. 
It is already known to undertake automatic analyses of the image gradation 
of image originals to be reproduced by evaluating image values of the 
image original and to employ the results of analysis for identifying 
image-dependent pre-setting values for the correction of the image 
gradation. The operator can evaluate the result of the image gradation 
analysis and can directly transfer the resulting pre-setting values into 
the image input device or can modify or correct them on the basis of 
measurement functions in order to undertake an optimum setting. The 
operator is thus relieved of routine jobs and can concentrate on the 
processing of image originals wherein additional global or selective color 
corrections are required for improving the reproduction quality. 
The known methods for the analysis of image gradation of image originals 
are based on histogram modification methods with identification of 
image-critical regions of the image original based on high-pass filtering 
techniques. 
In the histogram modification methods, the contrast changes are implemented 
on the basis of the frequency distribution (histogram) of the image values 
(for example, luminance values). A gradation characteristic 
(transformation characteristic) is generated from the histogram by an 
accumulation of the histogram values. The image values of an original 
image are resorted such via this gradation characteristic such that the 
histogram of the processed image assumes a defined path. 
This procedure has the disadvantage that background and foreground regions 
low in structures and unimportant to the image unduly alter the course of 
the histogram and, thus, unduly alter the gradation correction as well. 
Before applying a histogram modification method, the image-critical 
foreground and background regions must therefore be separated from the 
image-insignificant regions of the image original. 
In the methods for identifying image-critical regions of the original by 
high-pass filtering (LaPlace filtering or the like), however, only picture 
elements wherein the high-pass filter signal upwardly crosses a threshold 
are utilized for the calculation of the frequency distribution. This 
procedure, however, is extremely calculation-intensive and, thus, time 
consuming. Moreover, the identification of the frequency distribution from 
the edge information of the image is frequently unbeneficial. 
The known methods for the analysis of the image gradation in image 
originals also have the further disadvantage that they do not allow any 
reliable identification of the optimum setting values for an optimally 
broad spectrum of image originals, so that no simple, fast and 
standardized parameterizations of image input devices are possible. 
The known methods for image gradation analysis in color originals are based 
on the color values R, G, B of the device-dependent RGB color space 
acquired from the respective image input device. The analysis of the image 
scope and of the color cast are undertaken with direct reference to the 
color values R, G, B. A luminance signal derived from the color values R, 
G, B is frequently employed for the analysis of the image gradation. 
It is therefore likewise considered disadvantageous that the known analysis 
methods must be respectively adapted to the properties of the color values 
R, G, B of the respective image input devices given the connection of 
different image devices. 
The known analysis methods, moreover, are calculation-intensive, since the 
color values R, G, B acquired with the image input devices must be 
resolved into two color components for a color cast analysis and must also 
be resolved into a luminance component for an image scope analysis or an 
image gradation analysis. 
SUMMARY OF THE INVENTION 
The present invention provides improved methods and apparatus for analysis 
and correction of the image gradation in image originals (black-and-white 
originals, color originals) that work faster, more simply and more 
precisely than prior art methods and apparatus. 
For this purpose, the invention provides that: 
an image original to be analyzed is geometrically subdivided into a 
plurality of sub-images; 
a frequency distribution of an image value, preferably a luminance 
component of color values, in a corresponding sub-image is separately 
identified in a sub-image histogram for every sub-image; 
the sub-image histograms of the individual sub-images are evaluated and 
sub-images critical to the image for image gradation are identified based 
on the evaluation; 
an aggregate histogram that corresponds to the frequency distribution of 
the image values, or, respectively, of the luminance component of the 
color values, in the image-critical sub-images is calculated from the 
sub-image histograms of the image-critical sub-images; and 
a correction curve for the correction of an image gradation characteristic 
of the image original for the purpose of contrast correction is calculated 
from the aggregate histogram according to a histogram modification method. 
It is preferably provided that the evaluation of the sub-image histograms 
for the identification of the image-critical (structure-rich) sub-images 
takes place with the assistance of statistical histogram parameters. 
It has proven expedient that the histogram parameter of "scatter", i.e., 
standard deviation (SDev), and the histogram parameter of "relative area 
proportion of the most frequent image values" (FLAnt) of a sub-image 
histogram are utilized for the evaluation of the sub-image histograms, 
whereby the histogram parameter of "scatter" (SDev) is a measure for the 
mean deviation of the image values from the average of the histogram 
distribution of the sub-images, and the histogram parameter of "relative 
area proportion of the most frequent image values" (FLAnt) is a measure 
for the structure in image regions of the sub-images. 
The identification of the image-critical sub-images respectively occurs 
according to a classification pattern by comparing the histogram parameter 
of "scatter" (SDev) and the histogram parameter of "relative area 
proportion of the most frequent image values" (FLAnt) with thresholds 
(SwSDev, SwFLAnt) that are selectable for the image original. 
In an embodiment of the invention, a sub-image is advantageously classified 
as image-critical according to a classification pattern when the value of 
the histogram parameter "scatter" (SDev) of the sub-image is greater than 
a previously prescribed threshold (SwSDev) and the value of the histogram 
parameter "relative area proportion of the most frequent image values" 
(FLAnt) of the sub-image is lower than the prescribed threshold (SwFLAnt). 
In an embodiment of the invention, it is advantageous that the threshold 
(SwSDev) for the histogram parameter "scatter" (SDev) and/or the threshold 
(SwFLAnt) for the histogram parameter of "relative area proportion of the 
most frequent image values" (FLAnt) are respectively selected dependent on 
the properties of the image original. 
In an embodiment of the invention, the threshold (SwSDev) for the histogram 
parameter of "scatter" (SDev) is calculated from the frequency 
distribution of the values of the histogram parameter of "scatter" (SDev). 
In an embodiment of the invention, it has proven expedient to calculate the 
histogram parameter of "scatter" (SDev) and the histogram parameter of 
"relative area proportion of the most frequent image values" (FLAnt) of 
the sub-images by statistical evaluation of the corresponding sub-image 
histograms. 
The histogram parameter of "scatter" (SDev) for a sub-image preferably is 
calculated by means of the following steps: 
calculating the plurality (N) of image values for the sub-image from the 
corresponding sub-image histogram (Hi(i), i, . . . , M) according to the 
following equation: 
##EQU1## 
whereby (H(i)) corresponds to the plurality of picture elements having 
the image value (i) in the sub-image; 
calculating a mean of the frequency distribution from the sub-image 
histogram (Hi(i), i=1, . . . , M) according to the equation: 
##EQU2## 
calculating a variance (Var) according to the equation: 
##EQU3## 
and identifying the histogram parameter of "scatter" (SDev) according to 
the equation: 
##EQU4## 
The histogram parameter of "relative area proportion of the most frequent 
image values" (FLAnt) for a sub-image is expediently calculated according 
to the following steps: 
calculating the plurality N of image values for the sub-image from the 
corresponding sub-image histogram (Hi(i), i=1, . . . , M) according to the 
following equation: 
##EQU5## 
whereby (H(i)) corresponds to the plurality of picture elements having 
the image value (i) in the sub-image; 
resorting the histogram values (Hs(j) of the corresponding sub-image 
histogram (H(i)) in descending order of the frequency to form a new 
frequency distribution (Hs(i)); 
prescribing the plurality (n) of histogram values (Hs(i)) to be 
accumulated; and 
calculating the histogram parameter "relative area proportion of the most 
frequency image values" (FLAnt) according to the equation: 
##EQU6## 
It has also proven expedient to classify the sub-images sequences by means 
of the following steps: 
selecting the thresholds (SwSDev, SwFLAnt) for the histogram parameters of 
"scatter" (SDev) and of "relative area proportion of the most frequency 
image values" (FLAnt); 
calculating the histogram parameters of "scatter" (SDev) and of "relative 
area proportion of the most frequent image values" (FLAnt) for all 
sub-images; and 
evaluating the calculated histogram parameters of "scatter" (SDev) and of 
"relative area proportion of the most frequent image values" (FLAnt) 
according to the classification pattern. 
In an embodiment of the invention, the correction curve G=f(L) is 
calculated by an accumulation or summation of the histogram values (Hi) of 
the aggregate histogram according to the following equation: 
##EQU7## 
The accumulation is respectively implemented between the minimum value 
(Lmin) and the maximum value (Lmax) of the luminance scope of the image 
original. 
In an embodiment of the invention, it has likewise proven advantageous to 
smooth the correction curve G=f(L) with a low-pass filtering technique. 
This smoothing of the correction curve G=f(L) is expediently implemented 
according to the "sliding mean" method, in that the values of the smooth 
correction curve G=f(L) are calculated as the weighted sum of neighboring 
values of the unsmoothed correction curve. 
In an embodiment of the invention, the degree of correction for achieving a 
variable contrast correction is selectable with a correction factor (k). 
For that purpose, it can be provided that: 
a histogram gradation (HG) that corresponds to a maximum degree of 
correction (100%) is calculated from the luminance histogram of the image 
original according to a histogram modification method; 
a linear gradation (LG) that corresponds to a minimum degree of correction 
(0%) is generated; and 
a correction gradation (KG) for the variable contrast correction is formed 
by addition with the correction factor (k) of selectable portions of the 
histogram gradation (HG) and of the linear gradation (LG). 
The formation of the correction gradation (KG) preferably occurs according 
to the following equation: 
EQU KG=k*HG+(1-k)*LG 
The correction factor (k) is respectively selected dependent on the 
properties of the image original. 
In an embodiment of the invention, an advantageous development arises 
wherein the correction factor (k) is selected depending on the degree of 
correction of a calculated contrast correction and/or depending on the 
path of the luminance distribution in the image original. 
In an embodiment of the invention, an RMS actual value (RMS.sub.grd) 
describing the maximum degree of correction is preferably calculated from 
the correction curve G=f(L) as a mean quadratic deviation of the histogram 
gradation (HG) from the linear gradation (LG), this being calculated 
according to the following equation: 
##EQU8## 
with: .delta..sub.i =deviation of a corrected image value (histogram 
gradation HG) from an uncorrected image value i (linear gradation (LG); 
N=plurality of deviations (.delta..sub.i). 
In an embodiment of the invention, an RMS rated value (Rmsi) is calculated 
as a prescribed value for the contrast correction according to a 
classification pattern by comparing statistical histogram parameters of 
"skewness" (Skew) and "Kurtosis" (Kurt) calculated from the aggregate 
histogram of the image-critical sub-images to prescribable thresholds 
(SSw, KSw). 
The correction factor (k) can then be formed as quotient from the RMS rated 
value (Rmsi) and the RMS actual value (RMS.sub.grd). 
In an embodiment of the invention, the analysis of the image gradation of 
an image original takes place on the basis of image values that are 
acquired by scanning the image original with a resolution (coarse scan) 
coarser than a resolution required for the reproduction of the image 
original (fine scan). 
In an embodiment of the invention, a preferred development in the analysis 
of color originals arises wherein: 
the image values (R, G, B) of a first color space allocated to the input 
device are transformed into the functionally corresponding image values 
(L*, a*, b*) of a second color space (reference color space; communication 
color space) that is independent of the first color space (14); and 
the analysis of the image gradation for calculating setting values for the 
image processing is implemented on the basis of the transformed image 
values (L*, a*, b*). 
In an embodiment of the invention, there is provided an apparatus for the 
analysis and correction of the image gradation of a color original by 
evaluating color values acquired by point-by-point and line-by-line, 
trichromatic scanning with an input device in apparatus and systems for 
color image processing, comprising: 
a color converter connected to input devices for conversion of the image 
values (R, G, B) of a first color space allocated to the input devices 
into functionally corresponding image values (L*, a*, b*) of a second 
color space that is independent of the first color space; 
an image processing unit for processing the transformed image values (L*, 
a*, b*) having an operating terminal and a communication unit for the 
intermediate storage of the processed image values (L*, a*, b*); and 
a master analysis unit connected to the image processing unit and to the 
operating terminal with which the analysis of the image gradation of an 
image original for the calculation of setting values for the image 
processing is implemented on the basis of the transformed image values 
(L*, a*, b*) of the second color space. 
These and other features of the invention will become clearer below in the 
following detailed description of the presently preferred embodiments and 
accompanying drawings.

DETAILED DESCRIPTION OF THE PRESENTLY PREFERRED EMBODIMENTS 
FIG. 1 illustrates a schematic diagram of a structure of a color image 
processing system. Input devices that scan point-by-point and line-by-line 
are represented by the scanner 1; devices that scan planarly are 
represented by a camera 2; and devices for producing chromatic graphic 
data such as, for example, graphic design stations are represented by a 
video input 3. Various output devices are represented by a monitor 4, a 
color separation recorder 5 or by a proof recorder 6. 
The color values R, G, and B of the respective device-dependent color 
spaces generated in the input devices 1, 2 and 3 are converted in an input 
color converter 7 into color values of a device-independent communication 
color space and are supplied to an image processing unit 8. The color 
conversion of the device-dependent color space into the communication 
color space occurs via a reference color system. 
The input color converter 7, for example, is constructed as a memory table 
LUT wherein the output color values are addressably stored to be 
addressable by the corresponding input color values. The table values are 
input into the input color converter 7 via an input 9. In addition, an 
input calibration of the color values is implemented in the color 
conversion. The input color converter 7, as shown in FIG. 1, can be a 
separate unit or can be a component part of an input device 1, 2 or 3 or 
of the image processing unit 8. 
In the image processing unit 8, the color corrections and geometrical 
processings desired by the operator are implemented on the basis of the 
transformed color values of the respectively employed communication color 
space. For that purpose, the image processing unit 8 is connected to an 
operating terminal 8a for the operator. The image processing unit 8 is 
also in communication with a communication unit 8b in which the color 
values to be processed can be intermediately stored. 
Further, a master analysis unit 8c is provided that is connected to the 
image processing unit 8 and to the operating terminal 8a. A preselection 
as to whether the master analysis is to occur with respect to the image 
gradation or, on the other hand, is also to occur with respect to color 
cast and/or color scope can be made at programming inputs of the master 
analysis unit 8c. 
Before the image gradation analysis, the color original to be analyzed is 
scanned point-by-point and line-by-line in the scanner 1 with a resolution 
(coarse scan) coarser than the resolution required for the actual 
reproduction (fine scan). The color values R, G, and B thereby acquired 
are digitized, are potentially pre-distorted according to a given function 
(Munsell), are converted in the color converter 7 into the color values of 
the selected communication color space 15, for example into the color 
values L*, a* and b*, and, finally, are stored in the communication unit 
8b. 
After this, the color values L*, a* and b* of the course scan are loaded 
from the communication unit 8b into the master analysis unit 8c and are 
investigated therein with respect to the image gradation according to 
mathematical and statistical methods. 
Image-dependent pre-setting values that are forwarded to the operating 
terminal 8a result from the analysis. The operator can directly transfer 
the resulting pre-setting values into the image processing unit 8 for 
image setting or, on the other hand, can modify or, respectively, correct 
them in order to achieve an optimum setting. 
After the image processing, the processed color values are read out from 
the image processing unit 8 and are converted into process color values in 
an output color converter 12 on the basis of an output color 
transformation, these process color values being supplied to the 
corresponding output devices 4, 5 and 6. A corresponding output 
calibration thereby occurs. 
FIG. 2 illustrates a block diagram of a communication model for a color 
image processing system. The XYZ color value system (CIEXYZ) standardized 
by the Commission Internationale de l'Eclairge (CIE) International 
Commission on Illumination! and that is based on the visual properties of 
the human eye can serve as the reference color system 13. The color values 
R, G, B of the device-specific RGB color space 14 of the input devices 1, 
2, 3 are transformed by an input calibration into the reference color 
system 13. The color values X, Y, Z of the reference color system 13 are 
transformed by mathematically defined transformations into color values of 
a selectable, device-independent communication color space 15 with which 
the image gradation analysis and the image processing occur. 
Advantageously, communication color spaces 15 that conform with sensation, 
preferably the CIELAB color space are employed for the image gradation 
analysis. After the image processing, the transformation of the processed 
color values of the corresponding communication color space 15 into the 
process color values of the device-specific RGB color space 16 or, 
respectively, CMYK color space 17 of the output devices 4, 5 and 6 occurs. 
FIG. 3 illustrates the CIE 1976 L*a*b* color space--referred to in short as 
CIELAB color space--that is equidistantly constructed approximately in 
conformity with sensation. The Cartesian coordinates of the CIELAB color 
space are allocated to the sensation-conforming quantities luminance L*, 
red-green chrominance a* (R-G) and yellow-blue chrominance b* (Y-B). The 
value range of the luminance L* extends from 100 for reference white 
through 0 for the absolute black. The value range of the chrominances a* 
and b* for colors emanating from an illuminated subject 
(non-self-luminesce perceived colors) extends from approximately -80 
through approximately +120. The reference white and the absolute black 
have a chrominance of 0. The derived quantities of overall chrominance c* 
(chroma) and hue angle h can be calculated from the a*b* chrominances. The 
value range of the chrominance c* lies between 0 (neutral or gray) and 
approximately +120. The hue angle h lies between 0 and 360 degrees with 
reference to the positive a* axis. 
The method of the invention for analysis and correction of image gradation 
in image originals is based on the following considerations. 
A satisfactory reproduction quality of an image original can usually 
already be achieved on the basis of the proper balancing of light image 
and dark image, on the basis of a color cast correction and by prescribing 
a standard image gradation. 
Further measures are required for a good or better reproduction quality. In 
this regard, details critical to the image must be selectively emphasized 
by an intensification in the corresponding tint value ranges, i.e., a 
contrast correction. This, however, can only occur at the expense of a 
reduction in contrast of tint value ranges that are unimportant to the 
image, for instance in the image foreground or image background. 
These contrast corrections, i.e. the luminance corrections of the image 
values, are undertaken based on the correction of the image gradation 
characteristic with a correction curve a path of which is respectively 
matched to the image content of the image original, whereby a corrected, 
steeper image gradation characteristic effects an intensification of 
contrast, and a corrected, flatter image gradation characteristic effects 
a reduction in contrast. 
What is important for a good contrast correction is a correct demarcation 
of the regions of the image original critical to the image from the 
regions of the original that are not critical for the image and to the 
corresponding definition of the path of the correction curve for a 
contrast correction. 
An analysis of the image gradation evaluates the luminance distribution of 
an image original in view of low-contrast regions that, however, are 
critical to the image, demarcates the position of the tint value ranges of 
these low-contrast image details and derives the contrast-enhancing, 
corrected image gradation characteristic adapted to the image original 
therefrom. 
The individual method steps A! through E! of the method for the analysis 
and correction of the image gradation in image originals (black-and-white 
originals and color originals) is set forth in greater detail below. 
Method Step A! 
For identifying the frequency distribution of the luminance values from the 
image-critical regions of the original, the image original to be analyzed 
is geometrically divided into sub-images in a first method step A!, for 
example into a sub-image matrix of 16.times.16 sub-images. 
Method Step B! 
In a second method step B!, a frequency distribution sub-image histogram 
of the image values of a black-and-white original or, respectively, the 
frequency distribution of the luminance component L* of the color values 
L*, a*, b* of a color original is calculated for every sub-image. 
Method Step C! 
In a third method step C!, the sub-image histograms of the individual 
sub-images are statistically evaluated and the sub-images that are 
image-critical for the image gradation of the image original are then 
classified on the basis of the respective results of the evaluation. 
Evaluation of the Sub-Image Histograms Step C1! 
The identification of the sub-images critical to the image and not critical 
to the image takes place, for example, with the assistance of the 
statistical histogram parameter SDev "scatter" or, respectively, "standard 
deviation" and of the histogram parameter FIAnt "relative area proportion 
of the most frequent image values", referred to in short as histogram 
parameter FIAnt "rel.area proportion". However, other histogram parameters 
can also be utilized. 
The histogram parameter SDev "scatter" is a measure for the average mean 
deviation of the image values from the mean of the histogram distribution. 
Sub-images having a low scatter or standard deviation probably contain 
less structure and thus are not critical to the image. Sub-images having a 
high value of scatter or standard deviation probably contain a great deal 
of structure and thus are critical to the image. 
A later classification into image-critical and image-noncritical regions 
ensues via a definable thresholding of the histogram parameter SDev 
"scatter" with a threshold value SwSDev. When the value of the histogram 
parameter SDev "scatter" of a sub-image is lower than the prescribed 
threshold SwSDev, then the sub-image is classified as being low in 
structure. 
A conclusion regarding a great deal of structure in the sub-image cannot be 
unambiguously derived from a high value of the histogram parameter SDev 
"scatter". This is true, for example given images with large-area image 
regions of different luminance that are low in structure (for example, 
bimodal histogram distributions). The histogram parameter FIAnt "rel.area 
proportion" then is utilized for recognizing initialization in this type 
of image. 
The histogram parameter FLAnt "rel.area proportion" serves as a measure of 
the "planarity" of the image original, i.e. for the proportion of 
low-structure image regions in the sub-image. The histogram parameter 
FLAnt "rel.area proportion" indicates the relative proportion of the most 
frequent image values with reference to the total number of image values 
in a sub-image. Sub-images having a high value of the histogram parameter 
FLAnt "rel.area proportion" probably contain little structure and thus are 
not considered critical to the image. Sub-images having a low value of the 
histogram parameter FLAnt "rel.area proportion" probably contain a great 
deal of structure and thus are critical to the image. 
The later classification into image-critical and image-noncritical regions 
with the assistance of the histogram parameter FLAnt "rel.area proportion" 
likewise ensues via a definable thresholding step utilizing a threshold 
value SwFLAnt. When the histogram parameter FLAnt "rel.area proportion" of 
a sub-image is higher than the prescribed threshold SwFLAnt, then the 
sub-image is classified as low-structure. 
For the later sub-image classification, the threshold SwSDev of the 
histogram parameter SDev "scatter" and the threshold SwFLAnt of the 
histogram parameter FLAnt "rel.area proportion" are first defined. The 
thresholds determine the division into the two parameter classes. Given 
image originals having much structure, i.e. when a great number of 
sub-images contains structure, a higher threshold can be selected higher. 
Given image originals having less structure, i.e. when a small number of 
sub-images contains structure, a lower threshold can be selected. 
For evaluating the sub-image histograms, the histogram parameter SDev 
"scatter" and the histogram parameter FLAnt "rel.area proportion" are 
calculated for every sub-image according to calculating methods for 
statistical evaluation of histograms. 
The histogram parameter SDev "scatter" is calculated in the following way: 
A sub-image composed of a sequence of image values x.sub.1, . . . x.sub.N. 
N references the total plurality of image values in the value range of the 
image values x.sub.i : 1, . . . M. H(i) is the plurality of image values 
having the value i in a sub-image. 
The plurality of image values N is first calculated: 
##EQU9## 
For the calculation of the histogram parameter SDev "scatter", the mean 
value of the frequency distribution is then first calculated, whereby the 
mean value of a frequency distribution is that image value around which 
the other image values of the distribution group. The mean value is 
generated by the following: 
##EQU10## 
Subsequently, the variance Var is defined: 
##EQU11## 
The histogram parameter SDev "scatter" derives therefrom as: 
##EQU12## 
The standard deviation or, respectively, variance is a measure for the 
average or mean deviation of the image values from the mean of the 
distribution. When the standard deviation is low, then the image values 
lie close to the mean on average (narrow frequency distribution). When the 
standard deviation is high, then greater deviations of the image values 
from the mean will be more frequent (broad frequency distribution). 
The histogram parameter FLAnt "rel.area proportion" is calculated in the 
following way: 
For calculating the histogram parameter FLAnt "rel.area proportion", the 
histogram values H(i) are first sorted in the descending sequence of the 
frequency.fwdarw.H.sub.s (i). By prescribing the plurality n of histogram 
values H.sub.s (i) to be accumulated, the histogram parameter FLAnt is 
calculated as: 
##EQU13## 
The histogram parameter FLAnt indicates the relative proportion S of the 
most frequent image values with reference to the total number of image 
values and is a measure for the "planarity" or "flatness" of the original, 
i.e. for the proportion of low-structure image regions in the original. 
After the calculation of the histogram parameter SDev "scatter" and FLAnt 
"rel.area proportion", the thresholds SwSDev and SwFLAnt are defined, as 
set forth below. 
It has proven advantageous to define the threshold SwSDev and/or the 
threshold SwFLAnt depending on the original in order to obtain an adequate 
plurality of image-critical sub-images for calculating the luminance 
histograms. 
The following process can be implemented for defining the threshold SwSDev 
for the histogram parameter SDev "scatter". 
For image-dependent definition of the threshold SWSDev, the frequency 
distribution of the values of the histogram parameter SDev "scatter" of 
the individual sub-images is utilized. 
For that purpose FIGS. 4A and 4B illustrate a frequency distribution of the 
histogram parameter SDev "scatter" for image originals having little 
structure (upper. FIG. 4A) and for image originals having much structure 
(lower. FIG. 4B). Differently defined thresholds S respectively separate 
the frequency distributions into two parts that can be interpreted as 
being separate frequency distributions. 
The "informational content" (entropy) is respectively calculated for 
separate frequency distributions, whereby the threshold S is shifted 
across the possible value range. The entropy function .PHI. (S) is defined 
as the sum of the entropies of the two individual, separate frequency 
distributions dependent on the threshold S shifted over the possible value 
range. 
For that purpose, FIG. 5 illustrates a typical course of an entropy 
function b (S). For example, that value S at which the entropy function 
.PHI. (S) has a maximum value or at which the entropy function .PHI. (S) 
achieves a percentage of the maximum value of, for example, 90% is then 
selected as the threshold SwSDev for the histogram parameter SDev 
"scatter". 
The following may be said regarding the definition of the threshold SwFLAnt 
for the histogram parameter FLAnt "rel.area proportion": 
For example, a fixed value can be prescribed for the threshold SwFLAnt of 
the histogram parameter FLAnt "rel.area proportion". However, the 
plurality of the most frequent image values to be accumulated is 
identified depending on the image scope (minimum/maximum value of 
luminance) in the calculation of the histogram parameter FLAnt. 
After the calculation of the histogram parameter SDev and FLAnt for all 
sub-areas, the histogram parameters SDev and FLAnt are recalled in and 
compared to the corresponding thresholds SwSDev and SwFLAnt for the 
classification of image-critical (structure-rich) and image-uncritical 
(structure-poor) sub-images. 
Sub-Image Classification Step C2! 
The classification of the sub-images can proceed according to the following 
classification pattern: 
______________________________________ 
Parameter Parameter "Scatter" 
"Rel. area proportion" 
SDev &lt; SwSDev 
SDev &gt; SwsDev 
______________________________________ 
FLAnt &gt; Sub-image Sub-image 
SwFLAnt Without Structure 
Without Structure 
FLAnt &lt; Sub-image Sub-image 
SwFLAant Without Structure 
With Structure 
______________________________________ 
Denoted in this classification pattern are: 
SDev=histogram parameter "scatter" 
FLAnt=histogram parameter "rel.area proportion" 
SwSDev=threshold for histogram parameter "scatter" 
SwFLAnt=threshold for histogram parameter "rel.area proportion". 
A sub-image that only contains structure is thus classified as 
image-critical when the value of the histogram parameter SDev "scatter" is 
higher than the prescribed threshold SwSDev and the value of the histogram 
parameter FLAnt is lower than the prescribed threshold SwFLAnt. 
The sub-image histograms of those sub-images that were classified as 
structure-rich according to the above classification pattern are utilized 
for the calculation of the aggregate histogram according to Method Step 
D!, and this is set forth below. 
Method Step D! 
In a fourth method step D!, an aggregate histogram that corresponds to the 
frequency distribution of the image values or, respectively, of the 
luminance component in the image-critical sub-images is calculated from 
the sub-image histograms of the subimages classified as image-critical. 
For that purpose, the functionally corresponding frequency values for 
every luminance stage L* are added together in the individual sub-image 
histograms of the image-relevant sub-images and the summed-up frequency 
values are defined a as new frequency distribution over the corresponding 
luminance values L* as aggregate histogram. 
FIG. 6A illustrates the trend of a prior art luminance histogram without 
classification of image-critical sub-images. FIG. 6B juxtaposes an image 
original 20. According to the prior art, the entire image original is 
utilized for the formation of the resulting luminance histogram 21, this 
being shown in FIG. 6A. 
FIG. 6C shows an example of a classification of image-critical sub-images 
and the trend of an aggregate histogram that results from the sub-image 
histograms of the image-critical sub-images. 
FIG. 6D again shows an image original 20 that was subdivided into 
sub-images 22 according to method step A!. According to method steps B! 
and C!, sub-image histograms produced for the sub-images 22 and 
image-critical sub-images are identified by evaluating the sub-image 
histograms. Image-critical subimages are marked in black by way of example 
in FIG. 6D. 
The aggregate histogram 23 formed according to method step D! is shown in 
the FIG. 6C. This aggregate histogram reproduces the frequency 
distribution of the luminance values L* from the image-critical regions of 
the original. 
The aggregate histogram is employed for the calculation of a correction 
curve G=f(L) in! method step E! for the correction of the image 
gradation characteristic for the purpose of contrast correction. 
Method Step E! 
In a fifth method step E!, a correction curve G=f(L) for a contrast 
correction is calculated from the aggregate histogram according to the 
histogram modification method. 
The histogram modification method is set forth below in greater detail. 
Histogram Modification Method 
Methods of histogram modification are fundamentally well-suited for the 
automatic calculation of a correction curve for contrast correction, since 
a characteristic curve for contrast correction matched to a specific image 
original can be independently calculated on the basis of a statistical 
image analysis and contrast sensation models or concepts. 
In the histogram modification methods, the contrast changes are implemented 
based on the frequency distribution of the image values (histogram). The 
image values of an original image are resorted via a transformation such 
that the histogram of the processed image assumes a specific course. 
A histogram modification method is set forth, for example, with reference 
to the example of a histogram equalization, this being implemented in the 
following steps: 
In a first step, the frequency distribution of the image values is 
identified. 
In a second step, a transformation characteristic that corresponds to the 
aggregate frequency of the frequency distribution is calculated by summing 
the histogram values. 
In a third step, the image values are transformed via the transformation 
characteristic. 
After the transformation of the image values with the gradation 
transformation characteristic, the histogram of the processed image 
exhibits a modified course or trend. 
In the ideal case of extremely small graduation (quantization) of the image 
values (continuous image values), the histogram is exactly equally 
distributed. Given a courser quantization of the image values (discrete 
image values), an equal distribution of the image values can no longer be 
achieved by the redistribution of the image value steps but the frequency 
peaks are broadened and highly flattened. 
FIGS. 7A-7F illustrates the principle of the method of histogram 
equalization with continuous image values (FIGS. 7A, 7C and 7E) and with 
discrete image values FIGS. 7B, 7D, and 7F). An input histogram 24, a 
transformation characteristic 25 and an output histogram 26 are 
respectively illustrated, the latter corresponding to the input histogram 
24 modified according to the transformation characteristic. 
The correction curve G=f(L) calculated according to the method of histogram 
equalization effects an intensification of contrast by spreading image 
value steps in the tonal value ranges of the frequent image values (steep 
characteristic curve) and effects a reduction in contrast by combining 
image value steps in the tonal value ranges of the less frequent image 
values (flat characteristic curve). 
Following this explanation of the method of a histogram modification or, 
respectively, histogram equalization, reference is now made again to 
method step E!. The determination of the correction curve G=f(L) 
according to method step E! for correcting the image gradation 
characteristic occurs according to the above-described method of histogram 
modification by accumulation of the histogram values H(i) of the aggregate 
histogram in the range LMin through LMax according to the equation: 
##EQU14## 
The accumulation of the histogram values H(i) is thereby implemented only 
between the analyzed, minimum and maximum values of the luminance scope of 
the original (light image and dark image values). 
FIGS. 8A and 8B illustrate a graphic illustration of the determination of 
the correction curve G=f(L) 28 between dark image and light image from the 
aggregate histogram 27. 
The smoothing of the correction curve G =f(L) ensues on the basis of a 
low-pass filtering according, for example, to the "sliding mean" method. 
According to this method, the values of the smoothed characteristic are 
calculated as the weighted sum of neighboring values of the unsmoothed 
characteristic. Due to the specific selection of the weighting factors, an 
optimum smoothing by a polynomial of the third order with minimum 
deviation in the quadratic mean is achieved in the averaging interval of, 
for example, 5 values. The weighting factors can be formed in the 
following way: 
##STR1## 
The course of the correction curve G=f(L) for the contrast correction is 
reproduced by a limited plurality of supporting values (for example, 16 
supporting values). The selection of the supporting values from the values 
of the smoothed characteristics ensues optimally equidistantly in visual 
terms. The contrast correction determined from the luminance histogram is 
calculated into the color image values as, for example, a pure luminance 
correction via a change of the neutral gradation. Supporting values 
between the light image values and dark image values are identified for 
the correction curve G=f(L). 
The actual contrast correction occurs in the image scanning devices 1, 2, 
3, in that the calculated correction curve G=f(L) is forwarded to the 
image scanner device and the image gradation characteristic deposited 
thereat, for example, in table memories LUT, is corrected according to the 
correction curve G=f(L). The image values acquired by a fine scan in the 
image scanner device are then utilized for the conversion according to the 
corrected image gradation characteristic. 
The employment of the correction curve G=f(L) usually leads to great 
contrast corrections in practice, these being frequently not desired. 
Variable Contrast Correction 
An advantageous development of the method is therefore comprised therein 
that the contrast correction is made variable with the assistance of a 
selectable correction factor k so that the degree of correction can be set 
via the correction factor k from a minimum (0%) through a maximum (100%). 
A variable contrast correction is set forth below with reference to FIGS. 
9A and 9B. First, a histogram gradation HG (30) is defined from the 
luminance histogram H (29) according to the method of histogram 
modification. The application of the histogram gradation HG (30) 
corresponds to the 100% degree of correction. Simultaneously, a linear 
gradation LG (31) is produced that corresponds to a 0% degree of 
correction. 
The variable contrast correction for an image original occurs via a 
correction gradation KG (32) that is formed by addition of gradation parts 
of the histogram gradation HG (30) selectable via the correction factor k 
and the linear gradation LG (31) according to the following equation: 
EQU KG=k* HG+(1-k)*LG 
The selectable gradation parts k*HG (33) and (1-k)*LG (34) are likewise 
illustrated in FIGS. 9A and 9B. 
The degree of correction is advantageously designed image-dependent, such 
that the correction factor k is respectively defined depending on the 
properties of the image original. 
The determination of an image-dependent correction factor k is based on the 
following principle: 
The mean quadratic deviation of the calculated course of the correction 
curve G=f(L) (maximum contrast correction) from the linear course of the 
correction curve G f(L) (minimum contrast correction) is a measure for the 
"visual" strength of the contrast correction. The mean quadratic deviation 
(root means square), referenced RMS value, is determined from the visually 
equidistant luminance image values L*. A high RMS value corresponds to a 
great contrast correction; a low RMS value corresponds to a lower contrast 
correction. 
The RMS value of the calculated course of the correction curve G=f(L), 
however, does not generally correspond to the visually necessary 
correction. The required degree of the contrast correction is generally 
dependent on the course of the frequency distribution of the luminance 
values. Image originals having highly one-sided histogram curves (too 
light/dark) usually require a more pronounced correction. Image originals 
having more balanced histogram curves usually require less of a correction 
or no correction. 
Whether a histogram distribution is more balanced or highly one-sided can 
be advantageously derived from the statistical histogram parameters 
"skewness" and "Kurtosis". 
The parameter "skewness" (symmetry coefficient) describes the inequality of 
the spikes or peaks in a histogram distribution. The parameter "Kurtosis" 
is a measure for the course (flat/peaked) of a histogram distribution. 
The calculation of the histogram parameters Skew "skewness" and Kurt 
"Kurtosis" occurs from the aggregate histogram of the classified, 
image-critical sub-images according to the following equations: 
Histogram Parameter Skew "Skewness", 
##EQU15## 
Histogram Parameter Kurt "Kurtosis", 
##EQU16## 
The histogram parameter Skew "skewness" (symmetry coefficient) describes 
the inequality of the spurs of a distribution, i.e. the differences of the 
positive and negative deviation of the image values from the mean. The 
symmetry coefficient is positive when the frequency distribution has long 
spurs toward high values. By contrast, the symmetry coefficient is 
negative when the frequency distribution has long spurs toward low values. 
For symmetrical frequency distributions, the symmetry coefficient is 
approximately zero. 
The histogram parameter Kurt "Kurtosis" is a measure for the course 
(flat/peaked) of a frequency distribution relative to the normal 
distribution. When the histogram parameter Kurt "Kurtosis" is small or, 
respectively, negative, then the frequency distribution exhibits a flat 
course (broad frequency distribution); when, by contrast, it is high, then 
the frequency distribution exhibits a peaked course (narrow frequency 
distribution). 
FIGS. 10A and 10B show various histogram distributions and values of the 
histogram parameters Skew "skewness" and Kurt "Kurtosis". 
The determination of the correction factor k is advantageously undertaken 
dependent on the strength of the calculated contrast correction (RMS 
value) and/or dependent on the course of the luminance distribution of the 
histogram parameters Skew "skewness" and Kurt "Kurtosis" according to the 
following steps. 
In a first step, the RMS actual value of the correction curve G=f(L) 
(histogram gradation) is calculated. The RMS actual value corresponds to a 
maximum degree of correction. 
The RMS actual value of the calculated correction curve G=f(L) (histogram 
gradation) is defined as root mean square of the histogram gradation (HG) 
from a linear gradation (LG). For this purpose, FIG. 11 shows the RMS 
value of a histogram gradation (HG). 
The calculation of the RMS actual value (RMS.sub.grd) of the histogram 
gradation (HG) ensues according to the following equation: 
##EQU17## 
wherein: .delta..sub.i =deviation of the corrected image value (histogram 
gradation HG) from an uncorrected image value i (linear gradation LG); 
n=plurality of deviations .delta..sub.i. 
In a second step, the RMS rated value is identified as RMS prescribed value 
for the contrast correction with reference to a classification pattern. 
The classification into three regions "balanced", "one-sided" and "highly 
one-sided" ensues by comparing the statistical histogram parameters Skew 
"skewness" and Kurt "Kurtosis" to defined thresholds SSW1, SSw2 or, 
respectively, KSw1, KSw2 as follows: 
Classification Pattern: 
______________________________________ 
Kurtosis 
Absolute 
&lt;KSws1 &gt;KSw1 &gt;KSw2 Value skewness 
______________________________________ 
Rms 1 Rms 2 Rms 3 &lt;SSw1 
Rms 2 Rms 3 Rms 4 &gt;SSw1 
Rms 3 Rms 4 Rms 5 &gt;SSw2 
______________________________________ 
The RMS prescribed values Rmsi therein denote the following for the 
contrast correction: 
Rms1=weak contrast correction 
Rms2=weak contrast correction 
Rms3=moderate contrast correction 
Rms4=moderate contrast correction 
Rms5=great contrast correction, 
wherein: 
SSw1, SSw2=thresholds of the histogram parameter "skewness" 
KSw1, KSw2=thresholds of the histogram parameter "Kurtosis". 
An RMS prescribed value Rms.sub.i for the necessary contrast correction 
derived from the image gradation analysis derives as result of the 
classification. 
In a third step, the required value of the correction factor k is then 
calculated from the RMS prescribed value Rmsi (RMS rated value) and from 
the RMS actual value RMS.sub.grd : 
##EQU18## 
The value of the correction factor k generally lies between 0.0 (minimal 
correction) and 1.0 (maximum correction). When the calculation of the 
correction factor yields values greater than 1.0, then the value is 
limited to 1.0. 
Although modifications and changes may be suggested by those skilled in the 
art, it is the intention of the inventors to embody within the patent 
warranted hereon all changes and modifications as reasonably and properly 
come within the scope of their contribution to the art.