JP4557087B2 - Image processing apparatus, image processing method, and recording medium - Google Patents

Description

  The present invention relates to an image evaluation method for evaluating an image based on actual image data such as a digital photographic image, a medium on which an image evaluation program is recorded, and an image evaluation apparatus.

  Various types of image processing are performed on real image data such as digital photographic images. For example, image processing such as increasing the contrast, correcting the color tone, or correcting the brightness. Such image processing is normally executable by a microcomputer, and an operator confirms an image on a monitor and selects necessary image processing, or determines image processing parameters. That is, the operator determines the characteristics of the image and selects or executes various operations.

  In recent years, various image processing techniques have been proposed and are actually effective. However, humans must still be involved when it comes to how much processing is performed with which technique. This is because it has not been possible to determine what is important in the digital image data to be subjected to image processing.

  For example, when image processing for correcting brightness is considered, it is assumed that automatic processing is performed in which bright correction is performed when the average of the entire screen is dark, and dark correction is performed when the average is bright. Here, it is assumed that there is actual image data of a human image taken at night. Although the background is almost completely dark, it is assumed that the person was able to photograph well. If this live-action image data is automatically corrected, the background will be completely dark and will be corrected brightly, resulting in a daytime image.

  In this case, if a person is involved, focus only on the portion of the person image. Then, a correction is selected so that the person image is slightly brighter if the person image is dark, and conversely, if the person image is too bright due to an effect such as flash.

  In view of such a problem, the present applicant has proposed an invention for determining an important part in an image. In the present invention, it is assumed that the original subject (object) should exist in a sharp portion of the image, and pixels with a large degree of change are determined as objects by paying attention to the degree of change in the image at each pixel. is doing.

  However, even if there is a sharp part of the image, the area of the part is small, and image processing based on the background part may be preferable, and there is a possibility that deviation occurs only with one evaluation standard. In addition, there still remained a need to determine which evaluation criteria should be adopted.

  The present invention has been made in view of the above problems. An image evaluation method and an image evaluation program that can flexibly make it easy to use an image evaluation result when evaluating an image as a premise of image processing. An object is to provide a recorded medium and an image evaluation apparatus.

In order to achieve the above object, an image processing apparatus of the present invention provides:
An image processing apparatus for evaluating image data,
Image data input means for inputting image data;
The pixels included in the image data, extract with multiple sampling method corresponding to a plurality of the evaluation criterion prepared in advance as a criterion determinable image the circumstances of an object included in the image, the sampling technique A counting means for obtaining a counting result for each,
A first approach the intended image evaluation in the plurality of commentary divalent group criteria for confirmed one evaluation criteria the determined, a second method intended for image evaluation without going through determination of the plurality of evaluation criteria Image evaluation input means for selecting,
When the front Symbol first approach is selected, the plurality of the counting result for each sampling method, summed with weighting defined corresponding to the determined criteria from the plurality of evaluation criteria, and the sum to evaluate the image data on the basis of the summation result of the counting result, the pre-Symbol second approach is chosen, the counting result for each of the plurality of sampling techniques, using the counting result for each of the plurality of sampling techniques summed with the weighted calculated Te, characterized in that a picture image evaluation means for evaluating the image data on the basis of the summation result of the counting result obtained by the sum.

In the invention according to claim 1 configured as described above, image data is input, and pixels included in the image data of each pixel are prepared in advance as image evaluation criteria that can be determined according to the condition of the subject included in the image. It has been extracted by the sampling method of multiple corresponding to a plurality of the evaluation criteria to determine the counting result for each said sampling technique. Then, a first approach the intended image evaluation in the plurality of commentary divalent group criteria for confirmed one evaluation criteria the determined second intended image evaluation without going through determination of the plurality of evaluation criteria When any one of the methods is selected and the first method is selected , the aggregation result for each of the plurality of sampling methods is determined using a weight determined in accordance with the evaluation criteria determined from the plurality of evaluation criteria. summed to evaluate image data based on the summation result of the summed said counting result, the pre-Symbol second approach is chosen, the counting result for each of the plurality of sampling techniques, each of the plurality of sampling techniques the aggregation result summed with a weighting calculated using the, we evaluate the image data on the basis of the summation result of the counting result obtained by the sum.

In other words, there are appropriate evaluation criteria for evaluating the image with a sharp subject of the image as an object, such as portrait, and there are also appropriate evaluation criteria for evaluating the image with the background as an important object, calculated count results based on these plurality of evaluation criteria, on the basis of the determined total result, evaluating the image data by an automatic processor.

Here, the total result may be anything that can be used to determine the characteristics of the image, and it is not necessary to obtain a result that specifically identifies the type of the image. For example, it includes an index such as a luminance histogram when determining whether an image is bright or dark, and it is not necessary to obtain a determination result that the image is a bright image or a dark image. Of course, in addition to brightness and darkness, it may be an index as to whether an image is sharp or may be an index for determining vividness.

In order to apply a plurality of evaluation criteria to the aggregated results, various methods that achieve substantially the same purpose can be adopted. For example, it is an example that all the pixels are weighted according to individual evaluation criteria and aggregated. However, since the amount of processing to be aggregated over all the pixels is increased, performed as a preferable example for such a situation, the upper Symbol image data Upon aggregate thinned out at a predetermined criteria, the sampling based on multiple criteria with aggregate Te may Rukoto to summing respective counting result with a predetermined weighting.

If sampled image data assuming that aggregated, by changing the criteria for how this sampling result to be able to adopt a plurality of reference, further, by summing by adjusting the weighting of each counting result As a result, the evaluation results based on the plurality of evaluation criteria are respectively weighted and evaluated.

As a basic example of such a sampling method , one sampling method is configured to uniformly sample and aggregate.

In this way, the image data is thinned out evenly, but the image is captured as a whole, so it can be said that it is an evaluation criterion suitable for determination of landscape photographs and the like.

On the other hand, as an example of an evaluation standard that can be applied both when the sampling method is used and when it is not used , one evaluation standard is aggregated by weighting evaluations for pixels with a large degree of change from adjacent pixels in each pixel. You can make it.

When configured as described above, detects a degree of change between adjacent pixels in each pixel, by aggregated larger picture element is varied degree using sampling techniques to aggregate by sampling, as a result, the degree of change It made a clear image portions large and the child aggregation.

  Since this evaluation criterion is evaluated with emphasis on sharp portions of the image, it goes without saying that it is suitable for determining an image such as a human image. Here, as a method of placing an emphasis on evaluation on an image having a large degree of change, weighting may be changed while counting, or only pixels having a large degree of change may be totaled.

Weighting of evaluation criteria is not necessarily must necessarily fixed, it can be configuration changes the weights for each criterion.

By changing the weights for evaluation criteria as described above Niso respectively, it is possible to derive the overall evaluation result corresponding to the image. In this case, various aspects are included, such as changing each weight individually, or preparing a plurality of combinations in advance and selecting the combinations.

In addition, the change of weighting itself is not performed by the operator but is realized based on image data. As an example , the weighting of the evaluation result is performed based on the evaluation result based on each evaluation criterion. The configuration is changed.

If configuration as described above, to obtain the evaluation results in the evaluation criteria, taking into consideration such adaptability respective evaluation criteria from the evaluation result to change the weighting of the evaluation results.

  Various methods can also be adopted when changing the weighting of the evaluation criteria using the evaluation results. For example, it is assumed that it is determined whether or not the image data of each pixel is to be subjected to the above-described sampling based on a certain evaluation criteria. For example, the number of pixels is used as one evaluation criterion, and weighting is increased when the number of pixels is large.

  The idea of the invention for evaluating an image by the above-described method includes various aspects. That is, it can be changed as appropriate, for example, by hardware or by software.

  In the case of software for image processing as an embodiment of the idea of the invention, it naturally exists on a software recording medium in which such software is recorded, and must be used.

For example, the present invention provides an image processing method in which image data is input and pixels included in the image data are prepared in advance as a plurality of image evaluation criteria that can be determined according to the state of a subject included in the image. extracted with multiple sampling method corresponding to the evaluation criteria, determine the counting result for each said sampling method, to confirm the one of the plurality of commentary diradical quasi intended image evaluation of the evaluation criteria the determined a first approach, waiting for selection of a second method intended for image evaluation without going through the confirmation of the plurality of evaluation criteria, the pre-Symbol first approach is selected, said each of the plurality of sampling techniques the aggregation result, summed with weighting defined corresponding to the determined criteria from the plurality of evaluation criteria, on the basis of the summation result of the summed said counting result to evaluate the image data, before Symbol second technique is selected Once, the counting result for each of the plurality of sampling techniques, summed with the weighted calculated using the counting result for each of the plurality of sampling techniques, based on the summation result of the counting result obtained by the sum images processing method of evaluating the image data may be considered. Further, according to the invention of the recording medium, an image data input function for inputting image data and a pixel included in the image data are prepared in advance as an image evaluation standard that can be determined according to the condition of a subject included in the image. extracted with multiple sampling method corresponding to a plurality of the criteria is, the aggregation function of obtaining an aggregate result for each said sampling method, assessment to confirm the one of the plurality of commentary divalent group quasi was the established a first approach the intended image evaluation of the reference, the second image evaluation input function of selecting the method, before Symbol first approach intended image evaluation without going through the confirmation of the plurality of evaluation criteria are selected that the, the counting result for each of the plurality of sampling techniques, summed with weighting determined in accordance with the valuation criteria determined from the plurality of evaluation criteria, the counting result obtained by the sum To evaluate the image data based on the calculation result, the previous SL second approach is chosen, the counting result for each of the plurality of sampling method was calculated using the counting result for each of the plurality of sampling techniques weighting It summed with, as a recording medium recording an image processing program for realizing a picture image evaluation function of evaluating the image data on the basis of the summation result of the counting result obtained by the sum, and it is possible to grasp the present invention .

  Of course, the recording medium may be a magnetic recording medium, a magneto-optical recording medium, or any software recording medium to be developed in the future. In addition, the duplication stages such as the primary duplication product and the secondary duplication product are equivalent without any question. In addition, even when the communication method is used as a supply method, the present invention is not changed, and the same applies to the case where data is written on a semiconductor chip.

  Furthermore, even when a part is software and a part is realized by hardware, there is no difference in the idea of the invention, and a part is stored on a software recording medium as needed. It may be in a form that is read appropriately.

1 is a block diagram of an image processing system to which an image processing apparatus according to an embodiment of the present invention is applied. 2 is a block diagram of specific hardware of the image processing apparatus. FIG. It is a schematic block diagram which shows the other application example of the image processing apparatus of this invention. It is a schematic block diagram which shows the other application example of the image processing apparatus of this invention. It is a flowchart which shows the image evaluation process part in the image processing apparatus of this invention. It is a figure which shows the state which moves the magnitude | size of image data, and a process target pixel. It is explanatory drawing in the case of representing the change degree of an image with each component value of a rectangular coordinate. It is explanatory drawing in the case of calculating | requiring the change degree of an image with the difference value in the adjacent pixel of a vertical axis | shaft direction and a horizontal axis direction. It is explanatory drawing in the case of calculating | requiring the change degree of an image between all the adjacent pixels. It is a figure which shows the area | region which changes a threshold value. It is a figure which shows a sampling period. It is a figure which shows the number of sampling pixels. It is a figure which shows the relationship between the image of the conversion origin, and the pixel sampled. It is a figure which shows the input screen of an image evaluation option. It is a figure which shows the condition which adds together an individual sampling result, changing weighting. It is a flowchart which shows the back | latter stage and image processing part of an image evaluation process. It is a figure which shows the edge part obtained by the edge part process and edge part process of luminance distribution. It is a figure which shows the range of the expansion of luminance distribution, and the reproducible luminance. It is a figure which shows the conversion table at the time of enlarging luminance distribution. It is a figure which shows the concept brightened by (gamma) correction. It is a figure which shows the concept darkened by (gamma) correction. It is a figure which shows the correspondence of the brightness | luminance changed by (gamma) correction. It is a flowchart in the case of carrying out saturation emphasis. It is the schematic of the total state of saturation distribution. It is a figure which shows the relationship between the saturation A and the saturation emphasis index S. It is a flowchart in the case of edge emphasis. It is a figure which shows a 5x5 pixel unsharp mask.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

  FIG. 1 is a block diagram illustrating an image processing system that performs image processing by executing an image evaluation method according to an embodiment of the present invention. FIG. 2 is a schematic block diagram illustrating a specific hardware configuration example. .

  In FIG. 1, an image input device 10 outputs real image data representing a photograph or the like as a dot-matrix pixel to the image processing device 20, and the image processing device 20 aggregates the image data through a predetermined process. An evaluation result is obtained, and the image processing is executed after determining the content and extent of the image processing based on the evaluation result. The image processing apparatus 20 outputs image processed image data to the image output apparatus 30, and the image output apparatus outputs the image processed image with dot matrix pixels.

  The image processing apparatus 20 totals image data in advance and obtains an evaluation result for the image. At this time, a plurality of evaluation criteria are adopted, and the image data is totaled separately, and the weighting is changed under a predetermined condition and added up. Therefore, the image processing apparatus 20 constitutes an image data evaluation unit.

  A specific example of the image input apparatus 10 corresponds to the scanner 11, the digital still camera 12, or the video camera 14 in FIG. 2, and specific examples of the image processing apparatus 20 include a computer 21, a hard disk 22, a keyboard 23, and a CD-ROM drive 24. A computer system including a flexible disk drive 25 and a modem 26 is applicable, and specific examples of the image output apparatus 30 are a printer 31, a display 32, and the like. In the case of the present embodiment, since the image is evaluated so as to correct the defect of the image or the like, actual image data such as a photograph is preferable as the image data. The modem 26 is connected to a public communication line, connected to an external network via the public communication line, and can be installed by downloading software and data.

  In the present embodiment, the scanner 11 and the digital still camera 12 as the image input device 10 output RGB (green, blue, red) gradation data as image data, and the printer 31 as the image output device 30 has a floor. As the tone data, CMY (cyan, magenta, yellow) or CMYK binary data obtained by adding black to this is required as input, and the display 32 requires RGB gradation data as input. On the other hand, an operating system 21a operates in the computer 21, and a printer driver 21b and a display driver 21c corresponding to the printer 31 and the display 32 are incorporated. The image processing application 21d is controlled by the operating system 21a to execute processing, and executes predetermined image processing in cooperation with the printer driver 21b and the display driver 21c as necessary.

  Therefore, the specific role of the computer 21 as the image processing apparatus 20 is to create RGB gradation data that has been subjected to optimum image processing while inputting RGB gradation data and evaluating the image, and the display driver 21c. Is displayed on the display 32 via the printer driver 21b, and is converted into binary data of CMY (or CMYK) via the printer driver 21b and printed on the printer 31.

  As described above, in this embodiment, a computer system is incorporated between image input / output devices to perform image evaluation and image processing. However, such computer system is not necessarily required, and image data The present invention can be applied to a system that performs various image processing. For example, as shown in FIG. 3, a digital still camera 12a incorporates an image processing device that performs image evaluation and image processing, and displays the converted image data on the display 32a or prints it on the printer 31a. May be. As shown in FIG. 4, in the printer 31b that inputs and prints image data without using a computer system, image evaluation is performed from image data input through the scanner 11b, the digital still camera 12b, the modem 26b, or the like. It is also possible to configure to perform image processing.

  The above-described image evaluation and accompanying image processing are specifically performed by the image processing program corresponding to the flowchart shown in FIG. The flowchart shown in the figure corresponds to the first stage of image evaluation in the image processing program, and executes processing for totaling image data according to a plurality of evaluation criteria to obtain a predetermined evaluation result.

  Here, two evaluation criteria employed in the present embodiment will be described. What is common is that all pixels are not targeted, but the pixels are thinned out according to a predetermined standard, and the luminance is summed up for the sampled pixels. Also, the difference is that one side samples pixels evenly while the other side selects and samples edge pixels. The result of the luminance aggregation will be described later, but the evaluation of the image can be changed by changing the so-called sampling method in this way. Sampling the pixels evenly means counting the brightness of the pixels of the entire image, and evaluating the distribution of the brightness of the image data as a whole image. This is a reference for evaluation that the contrast is narrow.

  On the other hand, since the edge pixel is a sharp part of the image, the luminance is counted for the pixels related to the original subject in the image. If the subject has sufficient brightness even if the background is dark, The evaluation result that the brightness of is sufficient is obtained. In this embodiment, an image is determined by appropriately combining these two evaluation criteria by selection by an operator or automatic processing.

  Referring to the flowchart shown in FIG. 5, in this image evaluation process, as shown in FIG. 6, the target pixel of the image data composed of pixels in a dot matrix is moved in the horizontal direction by sub-scanning in the vertical direction. Each pixel is counted based on whether it is a sampling target or not.

  When the image data is composed of pixels in a dot matrix, each pixel is represented by the gradation data representing the RGB brightness described above, and the same data between adjacent pixels at the edge portion of the image. The difference of becomes large. This difference is a luminance gradient, which is referred to as an edge degree. In step S110, the edge degree at each pixel is determined. When considering XY orthogonal coordinates as shown in FIG. 7, the vector of the degree of change of the image can be calculated by obtaining the X-axis direction component and the Y-axis direction component, respectively.

  In a digital image composed of dot matrix pixels, as shown in FIG. 8, the pixels are adjacent to each other in the vertical axis direction and the horizontal axis direction, and the brightness is represented by f (x, y). In this case, f (x, y) is R (x, y), G (x, y), B (x, y) that is each luminance of RGB, or the entire luminance Y (x, y). The relationship between R (x, y), G (x, y), and B (x, y), which are RGB luminances, and the overall luminance Y (x, y) is strictly However, conversion is impossible unless a color conversion table or the like is referred to, but a simple correspondence may be used as described later.

  In the example shown in FIG. 8, the difference value fx in the X direction and the difference value fy in the Y direction are

  It is expressed as Therefore, the magnitude of the vector with these components

  It is expressed as Of course, the edge degree is represented by | g (x, y) |. Note that the pixels are originally arranged in a grid pattern vertically and horizontally as shown in FIG. 9, and there are eight adjacent pixels when attention is paid to the center pixel. Accordingly, similarly, the difference between the image data of each adjacent pixel may be represented by a vector, and the sum of the vectors may be determined as the degree of image change.

  Since the edge degree is obtained for each pixel as described above, a pixel having a higher edge degree than a certain threshold value may be determined as an edge pixel. Considering empirical facts, the subject on which the focus is concentrated is often located at the center of the composition. Therefore, by adopting a mechanism in which a large number of pixels are extracted from the central portion, a more favorable effect can be obtained when it is used for determination of image processing.

  For this reason, as shown in FIG. 10, the threshold values Th1, Th2, and Th3 to be compared for each portion in the image may be different. Of course, in this example,

  The threshold value is lower in the portion closer to the center, and it is determined that the focus is concentrated even if the edge degree is relatively low.

  In step S120, the degree of change is compared with the threshold value to determine whether the degree of change is large. As a result of the comparison, if the edge degree is larger, it is determined that this pixel is an edge pixel, and the image data of that pixel is sampled and stored in the work area in step S130. The work area may be the RAM in the computer 21 or the hard disk 22.

  On the other hand, in parallel with the determination of the edge degree, in step S140, it is determined whether or not the target pixel is a target pixel for uniform sampling. Even if sampling is performed uniformly, it is necessary to sample to an extent within a certain error range. According to the statistical error, the error with respect to the number of samples N can be expressed as 1 / (N ** (1/2)). However, ** represents a power. Therefore, in order to perform processing with an error of about 1%, N = 10000.

  Here, the bitmap screen shown in FIG. 6 has the number of pixels of (width) × (height), and the sampling period ratio is

  And Here, min (width, height) is the smaller of width and height, and A is a constant. Further, the sampling period ratio here indicates how many pixels are sampled, and the pixels marked with ◯ in FIG. 11 indicate the case where the sampling period ratio = 2. That is, one pixel is sampled every two pixels in the vertical and horizontal directions, and every other pixel is sampled. The number of sampling pixels in one line when A = 200 is as shown in FIG.

  As can be seen from the figure, except for the case where the sampling period ratio = 1 in which sampling is not performed, when there is a width of 200 pixels or more, the number of samples is at least 100 pixels. Therefore, in the case of 200 pixels or more in the vertical direction and the horizontal direction, (100 pixels) × (100 pixels) = (10000 pixels) is secured, and the error can be reduced to 1% or less.

  Here, the reason for using min (width, height) as a reference is as follows. For example, as shown in FIG. 13 (a), when width >> height is satisfied and the sampling period ratio is determined by the longer width, as shown in FIG. 13 (b). In addition, in the vertical direction, only two lines of the upper end and the lower end may be extracted. However, if the sampling period ratio is determined based on the smaller one as min (width, height), thinning is performed so as to include the intermediate portion in the smaller vertical direction as shown in FIG. Will be able to. That is, sampling with a predetermined number of extractions is possible.

  In step S140, it is determined whether or not the target pixel is the sampling target while adopting such an equal sampling method. If the target pixel is the target, the image data is sampled in step S150.

  Sampling the image data in steps S130 and S150 means summing up the luminance based on the image data. As described above, in the present embodiment, the computer 21 handles RGB gradation data, and does not have a luminance value directly. Although it is possible to perform color conversion to the Luv color space in order to obtain the luminance, it is not a good idea because of problems such as the amount of computation. For this reason, the following conversion formula for directly obtaining the luminance from RGB used in the case of a television or the like is used.

  The luminance is aggregated as a histogram, and of course, the aggregation area in step S130 and the aggregation area in step S150 are separate. It should be noted that the number of pixels that are subject to aggregation is also aggregated together with the aggregation of luminance.

  In order to perform the above processing for each pixel of the image data, the pixel to be processed is moved in step S160, and the processing is repeated until it is determined in step S170 that all pixels have been completed.

  When the luminance is totalized for the target pixel by each sampling method, an image evaluation option is input in step S180. FIG. 14 shows an image evaluation option input screen displayed on the display 32, and three options of portrait, landscape photograph, and automatic setting are prepared as options.

  As shown in FIG. 15, the luminance histogram obtained by equal sampling and the luminance histogram obtained by edge pixel sampling are combined to generate a histogram for evaluating the image. It needs to be adjusted. Here, when the weighting coefficient k is adopted, the totaling result Dist_Sum for evaluation is calculated from the totaling result Dist_ave of the uniform sampling and the totaling result Dist_edg of the edge pixel sampling.

  It becomes. The weighting coefficient k becomes more important as it approaches “0”, and the subject becomes more important as it approaches “1”. For this reason, after selecting an option on the image evaluation option input screen shown in FIG. 14, the process branches based on the option in step S190, and when a portrait is selected, “k = 0.8” is set in step S192. When a landscape photograph is selected, “k = 0.2” is set in step S194.

  One option that remains to be selected is automatic setting. In this automatic setting, as described above, based on the number of edge pixels sampled, when the number of edge pixels is small, the weighting coefficient k is approximated to “0” when the number of edge pixels is small, and the number of edge pixels is large. Is considered to be a portrait and the weighting coefficient k is brought close to “1”. The edge pixel sampling number x_edg and the uniform sampling number x_ave are used, and the weighting coefficient is determined in step S196.

  To obtain a total result Dist_Sum for evaluation.

  In this way, the evaluation of the image is performed by obtaining the evaluation result Dist_Sum for evaluation. Of course, this determination result may be used for further determination. Basically, it may be changed as appropriate according to the image processing that uses the aggregation result.

  Thereafter, optimal image processing is determined and executed based on the result of the aggregation. FIG. 16 shows a flowchart for executing image processing for increasing contrast and correcting brightness as an example.

  The basic method for enlarging the contrast in the present embodiment is to obtain a luminance distribution based on image data, and if this luminance distribution uses only a part of the original gradation width (255 gradations). It is to expand the distribution.

  Therefore, in step S310, a histogram of the luminance distribution as the total result Dist_Sum is created from the above-described weighting coefficient k, and in step S320, the width to be enlarged is determined. In determining the enlargement width, consider obtaining both ends of the luminance distribution. The luminance distribution of a photographic image appears in a mountain shape as shown in FIG. Of course, there are various positions and shapes. The width of the luminance distribution is determined by where the both ends are determined. However, the point where the base is simply extended and the number of distributions becomes “0” cannot be the both ends. This is because the number of distributions may change around “0” in the base portion, and if it is statistically viewed, it changes while approaching “0” as much as possible.

  For this reason, in the distribution range, portions that have passed a certain distribution ratio from the side with the highest luminance and the side with the lowest luminance are defined as both ends of the distribution. In the present embodiment, as shown in the figure, this distribution ratio is set to 0.5%. Of course, this ratio can be appropriately changed. In this way, by cutting the upper end and the lower end by a certain distribution ratio, it is possible to ignore white spots and black spots caused by noise or the like. In other words, if such a process is not performed, if there is even a single point of white or black, it will be at both ends of the luminance distribution. Although it is “0” and the uppermost end is gradation “255”, such a thing can be achieved by setting a portion that is inward by 0.5% of the number of pixels from the upper end portion as an end portion. Disappear.

  In the actual processing, 0.5% of the number of sampled pixels is calculated, and the number of distributions is accumulated in order from the luminance value at the upper end and the luminance value at the lower end in the reproducible luminance distribution in order toward the inside. The luminance value that is a value of% is obtained. Hereinafter, this upper end side is called ymax, and the lower end side is called ymin.

  When the reproducible luminance range is “0” to “255”, the conversion destination luminance Y is obtained from the luminance y before conversion, the maximum value ymax and the minimum value ymin of the luminance distribution range based on the following equation. .

  However,

  In the above conversion equation, if Y <0, Y = 0, and if Y> 255, Y = 255. Here, a is an inclination and b can be said to be an offset. According to this conversion formula, as shown in FIG. 18, the luminance distribution having a narrow width can be expanded to a reproducible range. However, when the luminance distribution is expanded by making the maximum use of the reproducible range, the highlight portion may be white and the high shadow portion may be black. In order to prevent this, the reproducible range is limited in this embodiment. That is, the luminance value “5” is left as a range that does not expand to the upper end and the lower end of the reproducible range. As a result, the parameters of the conversion equation are as follows:

  In this case, conversion is not performed in the range of y <ymin and y> ymax.

  However, if the same enlargement ratio (corresponding to a) is applied, a very large enlargement ratio may be obtained. For example, in the twilight state in the evening, it is natural that the contrast range from the brightest part to the dark part is narrow, but as a result of trying to enlarge the contrast for this image, it is converted like a daytime image. It can be. Since such conversion is not desired, a restriction is set on the enlargement ratio so that a is not 1.5 (˜2) or more. As a result, twilight is expressed as twilight. In this case, processing is performed so that the center position of the luminance distribution does not change as much as possible.

  By the way, it is unreasonable to execute the conversion equation (Y = ay + b) every time the luminance is converted. This is because the range that the luminance y can take is only “0” to “255”, and it is possible to obtain the luminance Y after conversion corresponding to all the values that the luminance y can take in advance. It is. Therefore, it is stored as a table as shown in FIG.

  Formation of such a conversion table corresponds to the enlargement width determination process in step S320, and the image data can be changed. However, it is extremely effective to not only enhance the contrast by expanding the brightness range but also adjust the brightness together. Therefore, in step S330, the brightness of the image is determined and corrected. Generate parameters.

  For example, when the peaks of the luminance distribution are on the whole dark side as shown by the solid line in FIG. 20, it is better to move the mountains to the bright side as shown by the wavy lines, In the case where the peaks of the luminance distribution are close to the bright side as shown by the solid line in FIG. 21, it is preferable to move the peaks to the dark side as shown by the wavy lines.

  As a result of various experiments, in the present embodiment, the median ymed in the luminance distribution is obtained, and when the median ymed is less than “85”, the image is determined to be a dark image and γ correction corresponding to the following γ values is performed. Brighten.

  Or

  And

  In this case, even if γ <0.7, γ = 0.7. This is because if such a limit is not set, the night image becomes like the daytime. Note that if the image is too bright, the overall image becomes whitish and the contrast tends to be low, so that processing such as enhancement of saturation is suitable.

  On the other hand, when the median ymed is larger than “128”, the image is judged to be a bright image and darkened by γ correction corresponding to the following γ values.

  Or

  And In this case, even if γ> 1.3, a limit is set so that γ = 1.3 so as not to be too dark.

  This γ correction may be performed on the luminance distribution before conversion or on the luminance distribution after conversion. FIG. 22 shows a correspondence relationship when γ correction is performed. When γ <1, the curve swells upward, and when γ> 1, the curve swells downward. Of course, the result of such γ correction may be reflected in the table shown in FIG. 19, and the same correction is performed on the table data.

  Finally, in step S340, it is determined whether or not contrast correction and brightness correction are necessary. In this determination, the enlargement ratio (a) and the γ value are compared with appropriate threshold values, and if the enlargement ratio is larger or the γ value exceeds a predetermined range, it is determined that there is a necessity. If it is determined that there is a necessity, the image data is converted.

  When it is determined that image processing is necessary, the conversion based on the equation (9) is performed. The conversion equation of the same equation can also be applied in the correspondence relationship with the RGB component values, and the component value before the conversion For (R0, G0, B0), the converted component values (R, G, B) are

  Can also be sought. Here, corresponding to the luminance y and Y ranging from gradation “0” to gradation “255”, the RGB component values (R0, G0, B0), (R, G, B) are also in the same range. Therefore, it can be said that the luminance y and Y conversion tables described above may be used as they are.

  Accordingly, in step S350, the image data (R, G, B) after conversion is obtained by referring to the conversion table corresponding to the equations (18) to (20) for the image data (R0, G0, B0) of all pixels. The process will be repeated.

  By the way, in this case, the result of luminance aggregation is used as an evaluation criterion used for image determination, and contrast correction and brightness correction are performed. However, a specific example of image processing is not limited to this, and accordingly, There are various aggregation contents used as evaluation criteria.

  FIG. 23 shows a flowchart when image processing for saturation enhancement is executed.

  First, if the pixel data has saturation as its component element, the distribution can be obtained using the saturation value, but since it has only RGB component values, it is essentially saturation. Saturation values cannot be obtained without conversion to a color space where the values are direct component values. For example, in the Luv space as a standard color system, the L axis represents luminance (brightness), and the U axis and V axis represent hue. Here, in the U axis and the V axis, the distance from the intersection of both axes represents the saturation, so that (U ** 2 + V ** 2) ** (1/2) is substantially the saturation.

  For such color conversion between different color spaces, it is necessary to use interpolation calculation together with reference to the color conversion table storing the correspondence relationship, and the amount of calculation processing becomes enormous. In view of such a situation, in this embodiment, the standard RGB gradation data is directly used as the image data, and the saturation alternative value X is obtained as follows.

  Originally, the saturation is “0” when R = G = B, and becomes the maximum value when mixing with a predetermined ratio of one or two colors of RGB. Although it is possible to express the saturation directly and appropriately from this property, even if it is yellow which is a single color of red and a mixed color of green and blue according to the simple equation (21), the maximum saturation is obtained. It is “0” when the components are uniform. In addition, green and blue single colors reach about half of the maximum value. Of course,

  It is also possible to substitute for this formula.

  In step S410, the distribution of the histogram for the alternative value X of saturation is obtained separately while employing the above-described equal sampling and edge pixel sampling methods. In the equation (21), the saturation is distributed in the range of the minimum value “0” to the maximum value “511”, and is roughly distributed as shown in FIG. In the next step S420, a saturation index for this image is determined based on the aggregated saturation distribution. However, also in this case, the saturation index is derived individually from the total sampling result of the uniform sampling and the total result of the edge pixel sampling, and the saturation index is calculated by using the above-described weighting coefficient k.

  In deriving the saturation index, in the present embodiment, the range occupied by the upper “16%” as the distribution number is obtained in the range of the number of sampled pixels. Then, the saturation enhancement index S is determined based on the following equation, assuming that the lowest saturation “A” in this range represents the saturation of this image.

  That is,

  And FIG. 25 shows the relationship between the saturation “A” and the saturation enhancement index S. As shown in the figure, the saturation index S is large when the saturation “A” is small in the range from the maximum value “50” to the minimum value “0”, and small when the saturation “A” is large. It will change gradually.

  When emphasizing the saturation based on the saturation emphasis index S, if the image data has a saturation parameter as described above, the same parameter can be converted, but the RGB color space is adopted. In such a case, it can be said that it must first be converted into the Luv space, which is a standard color system, and shifted in the radial direction within the Luv space. However, it is necessary to convert RGB image data into image data in the Luv space and return to RGB again after saturation enhancement, and the amount of computation must be increased. Therefore, saturation enhancement is performed using RGB gradation data as it is.

  When each component is a component value of a hue component in which the respective components are roughly equivalent as in the RGB color space, if R = G = B, the color is gray and achromatic. Therefore, assuming that the component having the minimum value in each component of RGB is merely reducing the saturation without affecting the hue of each pixel, the minimum value in each component is determined from all the component values. It can be said that the saturation can be enhanced by subtracting and enlarging the difference value.

  First, a saturation enhancement parameter Sratio that is advantageous for calculation from the saturation enhancement index S described above,

  Asking. In this case, when the saturation enhancement index S = 0, the saturation enhancement parameter Sratio = 1 and no saturation enhancement is performed. Next, assuming that the component value of blue (B) in each component (R, G, B) of the RGB gradation data is the minimum value, conversion is performed as follows using this saturation emphasis parameter Sratio.

  As a result, it is not necessary to perform two-time color conversion between the RGB color space and the Luv space, so that the calculation time can be reduced. In this embodiment, the method of simply subtracting the minimum value component from the other component values for the achromatic component is adopted, but another conversion formula is adopted for subtracting the achromatic component. It doesn't matter if you do it. However, when only the minimum value is subtracted as in equations (29) to (31), there is an effect that the amount of calculation becomes easy because multiplication and division are not involved.

  Even when the equations (25) to (27) are employed, good conversion is possible, but in this case, when the saturation is emphasized, there is a tendency that the luminance is improved and the entire image becomes brighter. Therefore, the conversion is performed on the difference value obtained by subtracting the luminance equivalent value from each component value.

  First, since the amount of calculation becomes large if the color conversion to the above-described Luv space is performed in order to obtain the luminance, the following conversion equation for obtaining the luminance directly from RGB used in the case of a television or the like: Is used.

  Luminance Y is

  On the other hand, saturation enhancement

  And These addition / subtraction values ΔR, ΔG, ΔB are obtained by the following equation based on the difference value from the luminance. That is,

  And as a result,

  Can be converted as In addition, the preservation | save of a brightness | luminance is clear from following Formula.

  Further, when the input is gray (R = G = B), the luminance Y = R = G = B, so the addition / subtraction value ΔR = ΔG = ΔB = 0, and the achromatic color is not colored. . If Equations (39) to (41) are used, the luminance is preserved, and even if the saturation is enhanced, the overall brightness does not increase.

  When the saturation enhancement index Sratio is obtained as described above, it is compared with a predetermined threshold value in step S430 to determine whether the image needs saturation enhancement. If necessary, in step S440, image data is converted for all pixels based on equations (39) to (41).

  Therefore, while evaluating the image data based on a plurality of evaluation criteria in steps S410 and S420, the respective evaluation results are added together with a predetermined weight, and the hardware configuration and software for executing these are added. Thus, the image data evaluation means is configured.

  Moreover, it can also be applied to the evaluation of the edge degree on the premise of the image processing of the edge enhancement processing as an evaluation criterion for other images. FIG. 26 shows a flowchart of this edge enhancement processing. The edge degree is calculated by the above-described method, and in step S510, the edge degree is totaled separately by the uniform sampling method and the edge pixel sampling method while moving the target pixel. Then, by dividing the accumulated edge degree by the number of pixels, an average value of the edge degree based on each evaluation criterion is calculated. That is, the sharpness SL of the image is defined as follows, where the number of pixels is E (I) pix.

  It can be calculated as follows. In this case, it can be determined that an image with a smaller value of SL has a lower degree of sharpness (the image is blurred), and an image with a larger value of SL has a higher degree of sharpness (the image is clearly visible).

  Next, in step S515, a weighting coefficient k is determined by inputting an image evaluation option, and the edge degrees based on the respective sampling methods are weighted and added together.

  On the other hand, since the sharpness of the image is sensuous, the sharpness SL is similarly determined for the image data having the optimum sharpness obtained experimentally, and the value is set as the ideal sharpness SLopt. In step S520, the edge enhancement degree Eenhance is set to

  Asking. Here, the coefficient ks changes based on the size of the image. As described above, when the image data is composed of height dots and width dots in the vertical and horizontal directions, respectively,

  It is asking for. Here, min (height, width) indicates the smaller of the height dot and the width dot, and A is a constant “768”. Of course, these are obtained from the experimental results and can be changed as appropriate. However, basically, a larger result is obtained by increasing the degree of enhancement for a larger image.

  When the edge enhancement degree Eenhance is obtained in this way, it is compared with a predetermined threshold value in step S530 to determine whether edge enhancement is necessary. If it is determined that it is necessary, in step S540, all pixels are determined. Perform edge enhancement processing.

  In the edge enhancement process, the luminance Y ′ after enhancement with respect to the luminance Y of each pixel before enhancement is

  Is calculated as Here, Yunsharp is obtained by performing unsharp mask processing on the image data of each pixel. Here, unsharp mask processing will be described. FIG. 27 shows an unsharp mask 41 of 5 × 5 pixels as an example. This unsharp mask 41 uses the value of “100” at the center as the weight of the processing target pixel Y (x, y) in the matrix-like image data, and weights corresponding to the numerical values in the squares of the mask for the peripheral pixels. Used for accumulating. When using this unsharp mask 41,

  Is integrated based on the following equation. In equation (48), “396” is the total value of the weighting coefficients, and in the unsharp masks of different sizes, each is the total value of the cells. Mij is a weighting factor written in the grid of the unsharp mask, and Y (x, y) is image data of each pixel. Note that ij is indicated by the coordinate values of the horizontal and vertical columns with respect to the unsharp mask 41.

  The meaning of the edge emphasis calculation calculated based on the equation (47) is as follows. Yunsharp (x, y) is obtained by lowering the weight of the peripheral pixel with respect to the target pixel and adding it, so that it is so-called “unsharp” image data. What is smoothed in this way has the same meaning as that obtained by applying a so-called low-pass filter. Therefore, “Y (x, y) −Yunsharp (x, y)” has the same meaning as that obtained by applying the high-pass filter by subtracting the low frequency component from the original all components. When the high frequency component that has passed through the high-pass filter is multiplied by the edge enhancement degree Eenhance and added to “Y (x, y)”, the high frequency component is increased in proportion to the edge enhancement degree Eenhance. The result is an enhanced edge.

  Note that since it is a so-called edge portion of an image in consideration of a situation where edge enhancement is necessary, the calculation may be performed only when there is a large difference in image data between adjacent pixels. In this way, it is not necessary to perform an unsharp mask operation on image data portions that are not almost edge portions, and the processing is drastically reduced.

  The actual calculation is based on the luminance Y ′ after enhancement and the luminance Y before enhancement.

R′G′B ′ after conversion is

  It becomes possible to calculate as follows.

  Therefore, in this edge emphasis process, in steps S510 and S515, while evaluating the edge degree of the image based on a plurality of evaluation criteria, the respective evaluation results are summed with a predetermined weight, The image data evaluation means is configured by the hardware configuration and software for executing these.

  It is determined whether image processing is performed for each of the above-described contrast correction, brightness correction, saturation enhancement, and edge enhancement. However, it is not always necessary to make a determination as to whether or not to perform image processing. That is, the degree of emphasis is set for each, and image processing may be performed with the degree of emphasis set in this way.

  Next, the operation of the present embodiment configured as described above will be described.

  Assume that a photographic image is read by the scanner 11 and printed by the printer 31. Then, first, while the operating system 21a is operating on the computer 21, the image processing application 21d is started, and the scanner 11 is started to read a photograph. When the read image data is taken into the image processing application 21d via the operating system 21a, the processing target pixel is set to the initial position. Subsequently, in step S110, the edge degree is determined based on the expressions (1) to (3), and in step S120, the threshold value is compared with the edge degree. If the edge degree is larger, it is determined that the processing target pixel is an edge pixel, and the image data of the pixel is stored in the work area in step S130. In step S140, it is determined whether or not the processing target pixel is a target of equal sampling. If the target pixel is a target, the image data of the pixel is stored in another work area in step S150.

  The above processing is repeated until it is determined in step S170 that all the pixels have been executed while moving the processing target pixel in step S160.

  When execution is completed for all pixels, image data sampled according to different evaluation criteria is stored in each work area. In step S180, an option for image evaluation is input. If the operator can determine whether the image is a portrait or a landscape photo by looking at the image, one of them can be selected. If the operator cannot determine or if it is desired to automate all, an automatic setting is selected. When the portrait is selected, the weighting coefficient k is “0.8”, and the total result for the edge pixels is emphasized. When the landscape photograph is selected, the weighting coefficient k is “0.2”. The weighted coefficient k corresponding to the ratio of the edge pixels is set when the weighted result is uniformly sampled and automatic setting is selected. However, in any case, a plurality of evaluation criteria are adopted using the weighting coefficient k, and flexible evaluation independent of only one evaluation criterion is possible.

  In the present embodiment, the image data itself is stored in the work area, but it is not always necessary to store the image data in the work area in view of memory capacity and processing time. That is, since a histogram of luminance distribution and saturation alternative value distribution is finally created for the pixel to be sampled, the histogram information may be stored in advance in steps S120 and S150.

  When performing contrast correction and brightness correction automatically, a weight distribution coefficient is used to obtain a luminance distribution histogram in steps S120, S150, and S310, and in step S320, enlargement is performed based on equations (12) and (13). In addition to determining parameters for processing, parameters for lightness correction are determined based on equations (14) to (17) in step S330. In step S340, these parameters are compared with predetermined threshold values. If it is determined that image processing is to be performed, luminance conversion is performed based on the parameters in step S350. In this case, in order to reduce the calculation amount, a luminance conversion table shown in FIG. 19 is created first, and the image data is converted based on the equations (18) to (20).

  Thereafter, the image data subjected to image processing is displayed on the display 32 via the display driver 21c, and if it is satisfactory, the image data is printed by the printer 31 via the printer driver 21b. In other words, the printer driver 21b inputs RGB gradation data with edge enhancement, performs rasterization corresponding to the print head area of the printer 31 through predetermined resolution conversion, and color-converts the rasterized data from RGB to CMYK. Thereafter, the CMYK gradation data is converted into binary data and output to the printer 31.

  Through the above processing, the image data of the photograph read through the scanner 11 is automatically subjected to optimum contrast correction and brightness correction, displayed on the display 32, and then printed by the printer 31. In other words, an image can be determined more flexibly by adopting a plurality of evaluation criteria, and optimal image processing such as contrast correction and brightness correction can be realized based on the evaluation result.

  On the other hand, not only such contrast correction and brightness correction, but also saturation enhancement and edge enhancement, the saturation and edge degree are sampled and aggregated according to a plurality of evaluation criteria, and the weighting coefficient is adjusted and added up. As a result, image processing is executed through a flexible determination that is not confined to a single evaluation criterion.

  As described above, the computer 21 serving as the center of the image processing samples the pixel image data based on different evaluation criteria in steps S120 and S140, and performs steps S192 to S192 based on the image evaluation options input in step S180. In step S196, the weighting coefficient k is determined, and the determined weighting coefficient k is used to add the tabulation results in step S310 to generate a luminance distribution histogram. An image is evaluated based on the result, and optimal image processing can be executed in steps S310 to S350.

DESCRIPTION OF SYMBOLS 10 ... Image input device 20 ... Image processing device 21 ... Computer 21a ... Operating system 21b ... Printer driver 21c ... Display driver 21d ... Image processing application 22 ... Hard disk 23 ... Keyboard 24 ... CD-ROM drive 25 ... Flexible disk drive 26 ... Modem 30 ... Image output device

Claims (5)

An image processing apparatus for evaluating image data,
Image data input means for inputting image data;
The pixels included in the image data, extract with multiple sampling method corresponding to a plurality of the evaluation criterion prepared in advance as a criterion determinable image the circumstances of an object included in the image, the sampling technique A counting means for obtaining a counting result for each,
A first approach the intended image evaluation in the plurality of commentary divalent group criteria for confirmed one evaluation criteria the determined, a second method intended for image evaluation without going through determination of the plurality of evaluation criteria Image evaluation input means for selecting,
When the front Symbol first approach is selected, the plurality of the counting result for each sampling method, summed with weighting defined corresponding to the determined criteria from the plurality of evaluation criteria, and the sum to evaluate the image data on the basis of the summation result of the counting result, the pre-Symbol second approach is chosen, the counting result for each of the plurality of sampling techniques, using the counting result for each of the plurality of sampling techniques summed with the weighted calculated Te, an image processing apparatus and a picture image evaluation means for evaluating the image data on the basis of the summation result of the counting result obtained by the sum. The image processing apparatus according to claim 1 .
The image processing device that obtains the aggregation result by using a sampling method that samples and aggregates pixel data equally as a sampling method of the plurality of sampling methods. The image processing apparatus according to claim 1 or billed to claim 2,
The totalization means uses a sampling method for sampling and totalizing pixels having a large degree of change from adjacent pixels in each pixel of the pixel data as an sampling method of the plurality of sampling methods to obtain the totalization result. apparatus. Enter the image data,
The pixels included in the image data, extract with multiple sampling method corresponding to a plurality of the evaluation criterion prepared in advance as a criterion determinable image the circumstances of an object included in the image, the sampling technique Find the total results for each
A first approach the intended image evaluation in the plurality of commentary divalent group criteria for confirmed one evaluation criteria the determined, a second method intended for image evaluation without going through determination of the plurality of evaluation criteria Wait for selection
When the front Symbol first approach is selected, the plurality of the counting result for each sampling method, summed with weighting defined corresponding to the determined criteria from the plurality of evaluation criteria, and the sum Evaluate the image data based on the total result of the aggregation results,
When the front Stories second approach is chosen, the counting result for each of the plurality of sampling techniques, summed with the weighted calculated using the counting result for each of the plurality of sampling techniques, the aggregate was the sum images processing method for evaluating the image data based on the results of the summation result. On the computer,
An image data input function for inputting image data;
The pixels included in the image data, extract with multiple sampling method corresponding to a plurality of the evaluation criterion prepared in advance as a criterion determinable image the circumstances of an object included in the image, the sampling technique A tabulation function that calculates the tabulation results for each
A first approach the intended image evaluation in the plurality of commentary divalent group criteria for confirmed one evaluation criteria the determined, a second method intended for image evaluation without going through determination of the plurality of evaluation criteria Image evaluation input function to select,
When the front Symbol first approach is selected, the plurality of the counting result for each sampling method, summed with weighting defined corresponding to the determined criteria from the plurality of evaluation criteria, and the sum to evaluate the image data on the basis of the summation result of the counting result, the pre-Symbol second approach is chosen, the counting result for each of the plurality of sampling techniques, using the counting result for each of the plurality of sampling techniques summed with the weighted calculated Te, the summed recording medium recording an image processing program for realizing a picture image evaluation function of evaluating the image data on the basis of the summation result of the counting result.

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