JP2012100031A - Image processing device and control method therefor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To perform appropriate dynamic γ correction processing using a γ correction curve with features of a left-eye image and features of a right-eye image reflected therein when correcting the stereoscopically viewable left-eye and right-eye images.SOLUTION: Histograms of stereoscopically viewable left-eye and right-eye images are created, a first dark-part feature amount as a gradation value on a low gradation side and a first bright-part feature amount as a gradation value on a high gradation side are detected from the histogram of the left-eye image, and a second dark-part feature amount as a gradation value on a low gradation side and a second bright-part feature amount as a gradation value on a high gradation side are detected from the histogram of the right-eye image. An integrated dark-part feature amount is determined using the first dark-part feature amount and the second dark-part feature amount, and an integrated bright-part feature amount is determined using the first bright-part feature amount and the second bright-part feature amount. A gradation correction parameter based on the integrated dark-part feature amount and the integrated bright-part feature amount is set, and gradations of the left-eye image and the right-eye image are corrected.

Description

  The present invention relates to an image processing apparatus capable of correcting gradations of a left-eye image and a right-eye image that can be stereoscopically viewed, and a control method thereof.

  A technique for suitably correcting the gradation of a display image by setting a gradation correction parameter (γ correction curve) for each frame based on a feature amount such as an average luminance value (APL) or a luminance histogram of the frame. Yes (Patent Document 1). Hereinafter, such gradation correction processing is referred to as dynamic γ correction processing.

Currently, there is a known stereoscopic system that performs stereoscopic viewing by displaying a three-dimensional (3D) image including a left-eye image and a right-eye image using binocular parallax on a two-dimensional display screen. Yes. However, the dynamic γ correction processing for each pair of left-eye image and right-eye image that can be viewed stereoscopically is performed on a frame-by-frame basis, so that the gradation of the left-eye image and the gradation of the right-eye image are large. When the difference occurs, stereoscopic viewing becomes difficult and eye fatigue increases.
Therefore, Patent Document 2 discloses a technique for correcting a left-eye image and a right-eye image based on an average value of characteristics such as luminance in the left-eye image and the right-eye image.

Japanese Unexamined Patent Publication No. 03-126377 JP 2007-151125 A

  However, in the above-mentioned Patent Document 2, there is no specific description as to what value is used as an average value of characteristics such as luminance in the image for the left eye and the image for the right eye. For example, using the luminance of the image for the left eye and the luminance of the image for the right eye, the average value of the luminance of the image for the left eye and the luminance of the image for the right eye is calculated, and created based on the gradation of the average value It is assumed that the image for the left eye and the image for the right eye are corrected using the γ correction curve. In this case, the luminance region in the vicinity of the average value is appropriately corrected in the left eye image and the right eye image, but the luminance region other than the average value cannot be corrected appropriately. is there. When a special region having a brightness larger than the average value is included only in the right-eye image, in the γ correction curve in the above example, many gradations are assigned to the gradation value of the average value, Since many gradations are not assigned to the gradation value of the brightness of the special area, it cannot be properly corrected.

  The present invention sets a gradation correction parameter that reflects the characteristics of the left-eye image and the right-eye image when correcting the left-eye image and the right-eye image that can be stereoscopically viewed, It is an object of the present invention to provide an image processing apparatus and a control method thereof that can appropriately correct gradations of an eye image and a right eye image.

  In order to achieve the above object, the present invention provides an image processing apparatus for displaying a left-eye image and a right-eye image on a display unit so as to enable stereoscopic viewing, and the input left-eye image and right-eye image. Histogram creation means for creating respective histograms of the images, and the first dark area feature value and the high gradation side that are the gradation values on the low gradation side from the histogram of the image for the left eye created by the histogram creation means A first detection unit that detects a first bright portion feature amount that is a gradation value, and a second dark portion that is a gradation value on the low gradation side from the histogram of the right-eye image created by the histogram creation unit. The integrated dark portion feature amount is determined using a second detection unit that detects a feature amount and a second bright portion feature amount that is a gradation value on the high gradation side, and the first dark portion feature amount and the second dark portion feature amount. , The first bright part feature and the second bright part feature Determining means for determining an integrated bright part feature value using a quantity; setting means for setting a gradation correction parameter based on the integrated dark part feature quantity and the integrated bright part feature quantity; and the setting set by the setting means Correction means for correcting the gradation of the image for the left eye and the image for the right eye using a gradation correction parameter.

  As described above, according to the present invention, when correcting the left-eye image and the right-eye image that can be stereoscopically viewed, the tone correction parameter that reflects the characteristics of the left-eye image and the right-eye image is used. It is possible to provide an image processing apparatus that can set and appropriately correct the gradation of the image for the left eye and the image for the right eye, and a control method thereof.

1 is a block diagram illustrating a functional configuration of an image processing apparatus according to Embodiments 1 and 2. FIG. It is a flowchart which shows the flow of a process with an image processing apparatus. It is a conceptual diagram explaining a division area. It is a figure explaining a dark part attention gradation and a bright part attention gradation. FIG. 6 is a diagram illustrating a specific example of a dark part attention gradation and a bright part attention gradation in each divided region of the left-eye image and the right-eye image according to the first embodiment. It is a figure which shows the setting method of (gamma) correction curve. It is a figure which shows the setting method of the (gamma) correction curve different from FIG. It is a figure which shows the specific example of the dark part attention gradation and the bright part attention gradation in the image for left eyes and the image for right eyes which concerns on Example 2. FIG. FIG. 10 is a block diagram illustrating a functional configuration of an image processing apparatus according to a third embodiment. It is a flowchart which shows the flow of a process with an image processing apparatus. It is a figure which shows the specific example of the APL value and difference in each division area of the image for left eyes, and the image for right eyes, and the APL value after gain adjustment. It is a figure which shows the setting method of (gamma) correction curve.

  Embodiments of the present invention will be described below with reference to the accompanying drawings.

Example 1
Specific examples of the image processing apparatus and the control method thereof according to the embodiment of the present invention will be described. FIG. 1 is a block diagram illustrating a functional configuration of the image processing apparatus according to the present embodiment. The image processing apparatus according to the present embodiment includes a left-eye image input terminal 101, a right-eye image input terminal 102, a left-eye image histogram creation unit 103, a right-eye image histogram creation unit 104, and a left-eye image attention. A gradation detection unit 105 and a right eye image attention gradation detection unit 106 are provided. Furthermore, the integrated attention gradation determination unit 107, the γ correction curve setting unit 108, the left-eye image γ correction unit 109, the right-eye image γ correction unit 110, the left-eye image output terminal 111, and the right-eye image. The output terminal 112 is configured.

  The flow of processing in the image processing apparatus will be described with reference to the flowchart of FIG. In step S <b> 11, the left-eye image histogram creation unit 103 and the right-eye image histogram creation unit 104 are stereoscopically viewable left-eye images input from the left-eye image input terminal 101 and the right-eye image input terminal 102. And a histogram for each of the right-eye images.

  Next, in step S12, the left-eye image attention gradation detection unit 105 and the right-eye image attention gradation detection unit 106 respectively obtain a dark-part attention gradation and a brightness from the left-eye image histogram and the right-eye image histogram. A partial attention gradation is detected. The method for detecting the dark part attention gradation and the bright part attention gradation will be described later.

  In step S <b> 13, the integrated attention gradation determination unit 107 determines the integrated dark part attention gradation using the dark part attention gradation of the left eye image and the dark part attention gradation of the right eye image, and determines the brightness of the left eye image. The integrated bright part attention gradation is determined using the part attention gradation and the bright part attention gradation of the right-eye image. The method for determining the integrated dark part attention gradation and the integrated bright part attention gradation will be described later.

  In step S <b> 14, the γ correction curve setting unit 108 sets a γ correction curve that is a gradation correction parameter based on the integrated dark part attention gradation and the integrated bright part attention gradation determined by the integrated attention gradation determination unit 107. In step S15, the left-eye image γ correction unit 109 and the right-eye image γ correction unit 110 use the γ correction curve set by the γ correction curve setting unit 108 to convert the left-eye image and the right-eye image. Processing for correcting gradation is performed. The corrected left-eye image and right-eye image are output from the left-eye image output terminal 111 and the right-eye image output terminal 112, respectively, and displayed on the display unit.

  A method of creating a histogram in the left-eye image histogram creation unit 103 and the right-eye image histogram creation unit 104 will be described. The left-eye image histogram creation unit 103 and the right-eye image histogram creation unit 104 receive the left-eye image signal and the right-eye image signal input from the left-eye image input terminal 101 and the right-eye image input terminal 102, respectively. Create each histogram.

  In the present embodiment, the left-eye image histogram creation unit 103 and the right-eye image histogram creation unit 104 divide the input left-eye image and right-eye image into a plurality of divided regions, respectively. Assume that a luminance histogram is created. For example, as shown in FIG. 3, the input image is divided into 4 × 4 16 divided regions. Each divided image can be distinguished by identification numbers d1 to d16 as shown in FIG. The number of divisions is not particularly limited to this, and may be 10 × 5, 5 × 3, or the like.

  In this embodiment, the created histogram is an 8-bit (256 gradation) luminance histogram having gradation values of 0 to 255. The gradation value may be 128 gradations or 512 gradations. In addition, the histogram is not for the luminance of the image, but is a histogram with a gradation of an average value of R (red) pixel value, G (green) pixel value, and B (blue) pixel value, and R (red) pixel value. It may be a histogram having the maximum gradation of the G (green) pixel value and the B (blue) pixel value. Also, the gradation of one of the pixel values of the R (red) pixel value, the G (green) pixel value, and the B (blue) pixel value, such as a histogram with the horizontal axis representing the gradation of the G (green) pixel value A histogram may be used. The present embodiment can be applied to any histogram representing such image characteristics.

  Next, a method for detecting a dark part attention gradation and a bright part attention gradation will be described. In the following, first, a method for detecting a dark part attention gradation and a bright part attention gradation in an image for the left eye will be described.

  The left eye image attention gradation detection unit 105 includes a dark portion attention gradation (first dark portion feature amount) and a dark portion attention gradation (first dark portion feature amount) from each luminance histogram in each unit region of the left eye image created by the left eye image histogram creation unit 103. A bright part attention gradation (first bright part feature amount) is detected. Here, the target gradation is a gradation value for which it is desirable to improve gradation. The target gradation is (1) a gradation value in which the frequency is equal to or greater than a predetermined threshold and (2) a maximum value in the luminance histogram, and (3) a variation amount of the frequency within a predetermined range including the gradation value is predetermined. Is a gradation value satisfying the above three conditions (1) to (3). Here, whether or not the frequency under the condition (2) is a maximum value is, for example, the frequency of the target gradation value whether or not it is the target gradation and m gradation values before and after that (m is 1). What is necessary is just to judge by comparing with the frequency of the above integer). The maximum value among the frequencies of the gradation values to be compared is set as the maximum value. Note that m tone values before and after the target tone value are m tone values smaller than the target tone value and m levels larger than the target tone value. Means the key value. The condition (3) is that when the image includes an area of the same color (same gradation value) (for example, a letterbox or a graphic or character such as data broadcasting), the frequency variation amount is locally increased. This is a condition for excluding key values. Whether or not the condition of (3) is satisfied is determined by the frequency of the target gradation value as to whether or not it is the target gradation, and the gradation value of ± n excluding the gradation value (n is an integer of 1 or more) The determination may be made by comparing with a value obtained by multiplying the sum of frequencies by a predetermined value.

  The dark part attention gradation in the luminance histogram is the attention gradation on the lower gradation side. In this embodiment, when the luminance histogram is 256 gradations, the minimum attention gradation among the attention gradations satisfying the above condition in the range of gradation values from 2 to 126 is set as the dark part attention gradation. When there is no attention gradation in the range of gradation values 2 to 126, the dark part attention gradation is set to 128.

  The bright part attention gradation in the luminance histogram is the attention gradation on the higher gradation side. In this embodiment, when the luminance histogram is 256 gradations, the maximum attention gradation among the attention gradations satisfying the above condition in the gradation value range of 130 to 253 is set as the bright part attention gradation. When there is no target gradation in the range of gradation values 130 to 253, the bright part attention gradation is set to 128. In the gradation values 0, 1, 127, 128, 129, 254, and 255, the frequencies of the two upper and lower gradation values are excluded from the attention gradation range in order to use the attention gradation detection.

  The dark part attention gradation (first dark part feature amount) which is the gradation value on the low gradation side detected from the luminance histogram and the bright part attention gradation (first light part feature amount) which is the gradation value on the high gradation side. As shown in FIG. As described above, the left-eye image attention gradation detection unit 105 detects the dark part attention gradation and the bright part attention gradation from each of the histograms in each divided region of the left-eye image. Similarly, the right-eye image attention gradation detection unit 106 determines a dark-part attention gradation (second dark-part feature amount) and a bright-part attention gradation (second bright-part feature) from each of the histograms in each divided region of the right-eye image. Amount).

  Next, a method for determining the integrated dark part attention gradation and the integrated bright part attention gradation will be described. The integrated attention gradation determination unit 107 includes a dark part attention gradation (first dark portion feature amount) in each divided region of the left eye image and a dark portion attention gradation (second dark portion feature amount) in each divided region of the right eye image. ) Is used to determine the integrated dark part attention gradation (integrated dark part feature amount). In addition, the bright portion attention gradation (first bright portion feature amount) in each divided region of the left eye image and the bright portion attention gradation (second bright portion feature amount) in each divided region of the right eye image are used. The integrated bright part attention gradation (integrated bright part feature value) is determined.

  In this embodiment, the minimum value of the dark part attention gradation in each divided region of the left eye image and the dark part attention gradation in each divided region of the right eye image is determined as the integrated dark part attention gradation. Further, the minimum value among the bright part attention gradation in each divided region of the left eye image and the bright part attention gradation in each divided region of the right eye image is determined as the integrated dark part attention gradation. A specific example is shown in FIG.

  FIG. 5A shows the dark part attention gradation in each divided area of the left eye image and the dark part attention gradation in each divided area of the right eye image, and FIG. 5B shows the left eye image. The bright part attention gradation in each division area and the bright part attention gradation in each division area of the right-eye image are shown. The gradation value 25 of the region d6 of the image for left eye, which is the minimum value among the dark part attention gradations in FIG. 5A, becomes the integrated dark part attention gradation. The gradation value 230 of the region d2 of the right-eye image, which is the maximum value among the bright part attention gradations in FIG. 5B, becomes the integrated dark part attention gradation.

  In the above example, the minimum value of the dark part attention gradation in each of the divided areas of the left eye image and the right eye image is obtained by integrating each of the divided dark part attention gradation, the left eye image, and the right eye image. The maximum value of the bright part attention gradation in FIG. 4 is set as the integrated bright part attention gradation, but is not limited thereto. For example, the integrated dark part attention gradation may be an average value of the minimum value of the dark part attention gradation in each divided region of the left eye image and the minimum value of the dark part attention gradation in each divided region of the right eye image. Similarly, the integrated bright portion attention gradation is an average value of the maximum value of the bright portion attention gradation in each divided region of the image for the left eye and the maximum value of the bright portion attention gradation in each division region of the image for the right eye. Also good. In the example of FIG. 5, the integrated dark part attention gradation is the average value 30 of the minimum value 25 of the dark part attention gradation of the image for the left eye and the minimum value 35 of the dark part attention gradation of the image for the right eye. The tone is an average value 215 of the maximum value 200 of the bright part attention gradation of the image for the left eye and the maximum value 230 of the dark part attention gradation of the image for the right eye.

  Next, a method for setting a γ correction curve will be described. The γ correction curve setting unit 108 sets a γ correction curve which is a gradation correction parameter based on the integrated dark part attention gradation and the integrated bright part attention gradation determined by the integrated attention gradation determination part 107. The γ correction curve to be set is a γ correction curve that enhances the gradation around the gradation values of the integrated dark part attention gradation and the integrated bright part attention gradation.

  FIG. 6 shows a γ correction curve when the integrated dark part attention gradation is the gradation value 25 and the integrated bright part attention gradation is the gradation value 230. The broken line in FIG. 6 is a γ correction curve when the gradation value of the input image is output as it is. On the other hand, the solid-line γ correction curve set by the γ correction curve setting unit 108 enhances the gradation around the gradation value 25 for the integrated dark portion attention gradation and the gradation value 230 for the integrated bright portion attention gradation. The γ correction curve is as follows.

  Further, as shown in FIG. 7, a γ correction curve is set from a dark curve γB for correcting low gradation data, a bright curve γW for correcting high gradation data, and an intermediate curve γM for correcting intermediate gradation. Also good. A method for setting a γ correction curve from the dark part curve γB, the bright part curve γW, and the intermediate curve γM will be described below.

  For example, as shown in FIG. 7, the dark curve γB is a curve for γ-correcting input data with gradation values 0 to 128. There are 126 types (i: 2 to 126, 128) of dark part curves γB (i). The dark part curve γB (i) is a γ correction curve that enhances the gradation in the gradation values in the range around the gradation value i. One dark part curve γB (i) is selected from 126 types of dark part curves γB (i) (i: 2 to 126, 128) according to the integrated dark part attention gradation. The integrated dark part attention gradation is determined using the dark part attention gradation in each divided region of the left-eye image and the dark part attention gradation in each divided region of the right-eye image. Take the value of When the integrated dark part attention gradation is the gradation value 25, the dark part curve γB (25) is selected.

  A bright curve γW is a curve for γ-correcting input data having gradation values of 192 to 255. As shown in FIG. 7, 125 types (i: 128, 130 to 253) of bright portion curves γW (i) are prepared. The bright part curve γW (i) is a γ correction curve that enhances the gradation in the gradation values in the range around the gradation value i. One bright portion curve γW (i) is selected from 125 types of bright portion curves γW (i) (i: 128, 130 to 253) according to the integrated bright portion attention gradation. The integrated bright portion attention gradation is determined using the bright portion attention gradation in each divided region of the left eye image and the bright portion attention gradation in each divided region of the right eye image. One of the values of 253 is taken. When the integrated bright part attention gradation is the gradation value 230, the bright part curve γW (230) is selected.

  By determining the γ correction curve as described above, a curve that assigns a gradation to a lower gradation value is generated when the integrated dark part attention gradation is lower, and a higher gradation value is obtained when the integrated bright part attention gradation is higher. A curve for assigning gradations to is generated. It is possible to improve the visibility of the dark part and the bright part by performing correction using the γ correction curve determined as described above.

  The intermediate curve γM is a curve for γ-correcting input data with gradation values 129 to 191. The intermediate γ curve γM has a conversion characteristic obtained by interpolating the gradation between the selected dark portion curve γB (i) and the bright portion curve γW (i) by linear interpolation. As described above, in the γ correction curve setting unit 108, the γ correction curve is set by the dark part curve γB, the bright part curve γW, and the intermediate curve γM.

  The left-eye image γ correction unit 109 and the right-eye image γ correction unit 110 correct the gradation of the left-eye image and the right-eye image using the γ correction curve set as described above. The corrected left-eye image and right-eye image are output from the left-eye image output terminal 111 and the right-eye image output terminal 112, respectively, and displayed on the display unit.

  As described above, according to the present embodiment, the γ correction curve for correcting the gradation of the left-eye image and the right-eye image that can be viewed stereoscopically reflects the characteristics of both the left-eye image and the right-eye image. Therefore, it is possible to perform appropriate γ correction processing without causing blackout or overexposure.

(Example 2)
In the first embodiment, when the histograms for the left-eye image and the right-eye image are created, the left-eye image and the right-eye image are each divided into a plurality of divided areas, and a histogram in each divided area is created. did. In this embodiment, a case will be described in which the left-eye image and the right-eye image are not divided. A description of the same processing as in the first embodiment will be omitted.

  The left-eye image histogram creation unit 103 and the right-eye image histogram creation unit 104 create respective histograms of the input left-eye image and right-eye image. In the present embodiment, as in the first embodiment, the created histogram is an 8-bit (256 gradation) luminance histogram having gradation values of 0 to 255.

  The left-eye image attention gradation detection unit 105 determines a dark-part attention gradation (first dark-part feature amount) and a bright-part attention gradation (from the luminance histogram of the left-eye image created by the left-eye image histogram creation unit 103. 1st light part feature-value) is detected. The method of detecting the dark part attention gradation and the bright part attention gradation from the histogram is the same as in the first embodiment. Similarly to the left-eye image attention gradation detection unit 105, the right-eye image attention gradation detection unit 106 also obtains the dark part attention gradation (second dark part feature amount) and the bright part attention gradation from the luminance histogram of the right-eye image. (Second bright part feature amount) is detected. The dark part attention gradation and the bright part attention gradation of the left eye image and the right eye image detected by the left eye image attention gradation detection unit 105 and the right eye image attention gradation detection unit 106, respectively. As shown in FIG.

  The integrated attention gradation determination unit 107 detects the dark part attention of the left eye image and the right eye image detected by the left eye image attention gradation detection unit 105 and the right eye image attention gradation detection unit 106, respectively. The integrated dark part attention gradation is determined from the gradation. Similarly, an integrated bright part attention gradation is determined from the bright part attention gradations of the left-eye image and the right-eye image.

  In this embodiment, the integrated dark part attention gradation may be the minimum value of the dark part attention gradation of the left eye image and the right eye image, or the dark part attention of the left eye image and the right eye image. An average value of gradations may be used. In the example of FIG. 8, the integrated dark part attention gradation is 25 when the dark part attention gradation of each of the left-eye image and the right-eye image is the minimum value, and 30 when it is the average value. The integrated bright part attention gradation may be the maximum value of the bright part attention gradations of the left eye image and the right eye image, or the bright part attention of the left eye image and the right eye image. An average value of gradations may be used. In the example of FIG. 8, the integrated bright portion attention gradation is 230 when the maximum value of the bright portion attention gradation of each of the left-eye image and the right-eye image is set, and 215 when the average value is set.

  The γ correction curve setting unit 108 sets and sets the γ correction curve, which is a gradation correction parameter based on the integrated dark part attention gradation and the integrated bright part attention gradation determined as described above, as in the first embodiment. The gradation of the left eye image and the right eye image is corrected using the γ correction curve.

  As described above, also in the present embodiment, the γ correction curve for correcting the gradation of the left-eye image and the right-eye image that can be viewed stereoscopically reflects the characteristics of both the left-eye image and the right-eye image. Since it is set, it is possible to perform appropriate γ correction processing without causing blackout or overexposure.

(Example 3)
Specific examples of the image processing apparatus and the control method thereof according to the embodiment of the present invention will be described. FIG. 9 is a block diagram illustrating a functional configuration of the image processing apparatus according to the present embodiment. Blocks that perform processing similar to the block diagram of FIG. 1 of the image processing apparatus according to the first and second embodiments are denoted by the same reference numerals.

  The image processing apparatus according to the present embodiment includes a left-eye image input terminal 101 and a right-eye image input terminal 102, a left-eye image division APL detection unit 301 and a right-eye image division APL detection unit 302, and gain value calculation. Unit 303 and integrated APL determination unit 304. Furthermore, the left-eye image gain adjustment unit 305, the right-eye image gain adjustment unit 306, the γ correction curve setting unit 307, the left-eye image γ correction unit 109, the right-eye image γ correction unit 110, and the left eye Image output terminal 111 and right-eye image output terminal 112.

  The flow of processing in the image processing apparatus will be described with reference to the flowchart of FIG. In step S31, the left-eye image segmentation APL detection unit 301 and the right-eye image segmentation APL detection unit 302 perform the left-eye image input from the left-eye image input terminal 101 and the right-eye image input terminal 102 and the right-eye image. Each ophthalmic image is divided into a plurality of divided regions. Then, an average luminance value in each divided area is detected. In this embodiment, the left eye image division APL detection unit 301 and the right eye image division APL detection unit 302 detect an average luminance value (APL value) in each divided region.

  In step S32, the gain value calculation unit 303 compares the APL values in the respective divided regions of the left eye image and the right eye image detected in step S31, and calculates the gain values of the left eye image and the right eye image. calculate. In step S33, the left-eye image gain adjustment unit 305 and the right-eye image gain adjustment unit 306 adjust the APL values in the respective regions of the left-eye image and the right-eye image based on the calculated gain values. . The method for calculating the gain value and the method for adjusting the APL value based on the gain value will be described later.

  In step S34, the integrated APL determination unit 304 determines the integrated dark part APL value and the integrated bright part APL value by using the APL values in the respective divided regions of the left-eye image and the right-eye image corrected by the gain value. A method for determining the integrated dark part APL value and the integrated bright part APL value will be described later.

  In step S35, the γ correction curve setting unit 307 sets a γ correction curve that is a gradation correction parameter based on the integrated dark part APL value and the integrated bright part APL value. A method for setting the γ correction curve will be described later. In step S36, the left-eye image γ correction unit 109 and the right-eye image γ correction unit 110 use the γ correction curve set by the γ correction curve setting unit 307 in the same manner as in the first and second embodiments. Processing for correcting the gradation of the image and the right-eye image is performed. The corrected left-eye image and right-eye image are output from the left-eye image output terminal 111 and the right-eye image output terminal 112, respectively, and displayed on the display unit.

  In the present embodiment, the left-eye image division APL detection unit 301 and the right-eye image division APL detection unit 302 detect the luminance value of each pixel of the left-eye image and the right-eye image. In this embodiment, the luminance value is detected with 8 bits (256 gradations) of gradation values 0 to 255.

  Assume that the left-eye image and the right-eye image are divided into, for example, 4 × 4 16 divided areas d1 to d16 as shown in FIG. 3 and the APL value of each divided area is detected. The left-eye image division APL detection unit 301 and the right-eye image division APL detection unit 302 use 0 to 7 as the luminance value of each pixel detected in 256 gradations, and set the APL value to 3 bits (8th floor). )). The number of image divisions is not particularly limited to this, and may be 10 × 5, 5 × 3, or the like, and the detection accuracy of the APL value may be, for example, 128 gradations or 256 gradations.

  FIG. 11A shows a specific example of the APL value of each divided region of the left eye image and the right eye image, and the difference between the left eye image APL value and the right eye image APL value in each divided region. . The APL value of each divided region of the left eye image and the right eye image is detected by the left eye image division APL detection unit 301 and the right eye image division APL detection unit 302. The difference between the left-eye image APL value and the right-eye image APL value in each divided region is obtained by the gain value calculation unit 303.

  A gain value calculation method and an APL value adjustment method based on the gain value will be described with reference to FIG.

  The gain value calculation unit 303 compares the left-eye image APL value and the right-eye image APL value in each divided region. In the divided areas having a difference equal to or greater than a predetermined threshold (biased), the number of divided areas where the left-eye image APL value is larger than the right-eye image APL value, and the right-eye image APL value is the left eye The number of divided areas larger than the image APL value for use is counted.

  In FIG. 11A, when the predetermined threshold value is 1, a divided region in which the left-eye image APL value is larger than the right-eye image APL value (left-eye image APL value−right-eye image APL value is positive and 1 or more). There are 13). In addition, there are two divided regions in which the right-eye image APL value is larger than the left-eye image APL value (left-eye image APL value-right-eye image APL value is negative and the absolute value is 1 or more).

  In this way, the difference between the number of divided areas where the left-eye image APL value is larger than the right-eye image APL value and the number of divided areas where the right-eye image APL value is larger than the left-eye image APL value is a predetermined value ( For example, if 2) or more exists and there is a bias, the gains of the left-eye image and the right-eye image are adjusted. This is because the difference in the feature amount between the image for the left eye and the image for the right eye is caused by the parallax in the 3D image, but it is considered that almost the same subject is reflected in a region in many images. This is because it is assumed that a certain amount of difference between the feature amounts of the image for use and the image for the right eye does not occur.

  The gain value calculation unit 303 calculates the average value of the left-eye image APL values of the divided areas in which the difference between the left-eye image APL value and the right-eye image APL value is within a certain range in the divided divided areas. Then, the gain value is calculated from the ratio with the average value of the right-eye image APL value. In the divided areas d1 to d9 and d10 to d16 having a bias in FIG. 11A, the left eye image of each divided area in which the difference between the left eye image APL value and the right eye image APL value is 1. The average value of the APL value and the average value of the right-eye image APL value are calculated. Specifically, the average value of the left-eye image APL values in the divided regions d2, d4 to d8, d10 to d14, and d16 in which the difference between the left-eye image APL value and the right-eye image APL value is 1 is 3 .83, the average value of the right-eye image APL value is 2.83. The ratio between the average value of the left-eye image APL value and the average value of the right-eye image APL value is (average value of the left-eye image APL value) / (average value of the right-eye image APL value) = 1.35.

  The gain value calculation unit 303 outputs 1 / 1.35 as a gain value to the left-eye image gain adjustment unit 305, and outputs 1 as a gain value to the right-eye image gain adjustment unit 306. By adjusting the gains of the left-eye image and the right-eye image using the gain value set in this way, it is possible to adjust a large bias in the feature amount of the left-eye image and the right-eye image. The left-eye image gain adjustment unit 305 and the right-eye image gain adjustment unit 306 use the gain value output from the gain value calculation unit 303 to adjust the gains of the left-eye image and the right-eye image. FIG. 11B shows the left-eye image APL value (after adjustment) and the right-eye image APL value (after adjustment) in each divided region of the adjusted left-eye image and right-eye image.

  A method for determining the integrated dark part APL value and the integrated bright part APL value will be described. The integrated APL determination unit 304 includes a left-eye image APL value in each divided region of the left-eye image and the right-eye image from the left-eye image division APL detection unit 301 and the right-eye image division APL detection unit 302. And the right-eye image APL value are output. Further, the gain value calculation unit 303 outputs the gain values of the left eye image and the right eye image.

  From the left-eye image APL value and the right-eye image APL value in each divided region of the left-eye image and right-eye image, the minimum APL value (first dark portion feature amount) and the maximum APL value in the left-eye image ( 1st light part feature-value) is determined. Further, the minimum APL value (second dark portion feature amount) and the maximum APL value (second bright portion feature amount) in the image for the right eye are determined. In FIG. 11A, the maximum APL value is 6 and the minimum APL value is 2 among the left-eye image APL values in each divided region of the left-eye image. The maximum APL value is 7 and the minimum APL value is 2 among the right-eye image APL values in each divided region of the right-eye image.

  The gain is adjusted using the gain values of the left-eye image and the right-eye image output from the gain value calculation unit 303 for the maximum APL value and the minimum APL value in the left-eye image and the right-eye image, respectively. . Since the gain value of the left-eye image is 1 / 1.35, the maximum APL value 6 of the left-eye image is 6 × 1 / 1.35 = 4 (rounded off after the decimal point), and the minimum APL value 2 is 2 × 1. /1.35=1. Since the gain value of the right-eye image is 1, the maximum APL value 7 of the right-eye image is adjusted to 7 × 1 = 7, and the minimum APL value 2 is adjusted to 2 × 1 = 2 (when the gain value is 1) Is equivalent to no gain adjustment).

  A gradation value 1 which is the smaller APL value of the minimum APL value (first dark portion feature amount) of the left-eye image after gain adjustment and the minimum APL value (second dark portion feature amount) of the right-eye image, The integrated dark part APL value (integrated dark part feature value) is used. The gradation value 7 which is the larger APL value of the maximum APL value (first bright portion feature amount) of the left-eye image after gain adjustment and the maximum APL value (second bright portion feature amount) of the right-eye image. Is an integrated bright part APL value (integrated bright part feature value).

  The method of determining the integrated dark part APL value and the integrated bright part APL value is not limited to the above. For example, the average value of the minimum APL value of the image for the left eye and the average value of the minimum APL value of the image for the right eye is integrated and the average value of the maximum APL value of the image for the left eye and the maximum APL value of the image for the right eye is integrated and brightened. It is good also as a part APL value.

  Further, the integrated APL determination unit 304 may not perform gain adjustment for the maximum APL value and the minimum APL value in the left-eye image and the right-eye image. In that case, the left-eye image APL value and the right-eye image APL value (shown in FIG. 11B) in the respective divided areas of the left-eye image and the right-eye image after gain adjustment, Obtained from the eye image gain adjustment unit 305 and the right eye image gain adjustment unit 306. Then, the maximum APL value and the minimum APL value in each image are determined from the acquired left eye image APL value and right eye image APL value in each divided region of the left eye image and right eye image after gain adjustment. To do.

  A method for setting a γ correction curve based on the integrated dark part APL value and the integrated bright part APL value will be described.

  The γ correction curve of this embodiment includes a dark part curve γB for correcting low gradation data, a bright part curve γW for correcting high gradation data, and an intermediate curve γM for correcting intermediate gradations. In this embodiment, it is assumed that the gradation resolution of image data is 3 bits (0 to 7 gradations).

  As shown in FIG. 12, the dark part curve γB is a curve for γ-correcting input data having gradation values of 0 to 128. Eight types (i: 0 to 8) of dark part curves γB (i) are prepared. The dark part curve γB (i) is a γ correction curve that enhances the gradation in the gradation values in the range around the gradation value i. One dark part curve γB (i) is selected from eight types of dark part curves γB (i) (i: 0 to 7) according to the integrated dark part APL value. Since the integrated dark portion APL value is determined using the minimum APL value of the left-eye image and the minimum APL value of the right-eye image, any one of 0 to 7 is detected when the APL value is detected with an accuracy of 3 bits. Takes the value of When the integrated dark portion APL value is the gradation value 1, the dark portion curve γB (1) is selected.

  A bright curve γW is a curve for γ-correcting input data having gradation values of 192 to 255. As shown in FIG. 12, eight types (i: 0 to 7) of bright portion curves γW (i) are prepared. The bright part curve γW (i) is a γ correction curve that enhances the gradation in the gradation values in the range around the gradation value i. One bright part curve γW (i) is selected from eight types of bright part curves γW (i) (i: 0 to 7) according to the integrated bright part APL value. Since the integrated bright portion APL value is determined using the maximum APL value of the left-eye image and the maximum APL value of the right-eye image, any one of 0 to 7 is detected when the APL value is detected with an accuracy of 3 bits. Take the value of When the integrated bright part APL value is the gradation value 7, the bright part curve γW (7) is selected.

  The intermediate curve γM is a curve for γ-correcting input data having gradation values of 128 to 192. The intermediate γ curve γM has a conversion characteristic obtained by interpolating the gradation between the selected dark portion curve γB (i) and the bright portion curve γW (i) by linear interpolation. As described above, in the γ correction curve setting unit 108, the γ correction curve is set by the dark part curve γB, the bright part curve γW, and the intermediate curve γM.

  As described above, the dark portion curve γB, the bright portion curve γW, and the intermediate curve γM are set. However, the gradation values corrected by each γ curve are not limited to those shown above. For example, the dark portion curve γB may correct input data with gradation values 0 to 3, the light portion curve γW with gradation values 6 to 7, and the intermediate curve γM with gradation values 3 to 6.

  By determining the γ correction curve as described above, a curve for assigning a gradation to a lower gradation value is generated as the integrated dark portion APL value is lower, and a gradation is increased to a higher gradation value as the integrated dark portion APL value is higher. A curve to assign is generated. It is possible to improve the visibility of the dark part and the bright part by performing correction using the γ correction curve determined as described above.

  In this embodiment, the gain value calculation unit 303 compares the APL value of the left-eye image and the APL value of the right-eye image and calculates a gain value when there is a difference equal to or greater than a predetermined threshold. Although the gain is adjusted, the gain may not be adjusted when the difference is equal to or less than a predetermined threshold.

  As described above, also in this embodiment, the γ correction curve for correcting the gradation of the left-eye image and the right-eye image that can be viewed stereoscopically reflects the characteristics of both the left-eye image and the right-eye image. Therefore, it is possible to perform appropriate γ correction processing without causing blackout or overexposure. Even when there is a difference between the APL value of the left-eye image and the APL value of the right-eye image, an appropriate γ correction process can be performed.

105 Image attention gradation detection unit for left eye 106 Image attention gradation detection unit for right eye 107 Integrated attention gradation determination unit 108 γ curve setting unit

Claims (11)

In an image processing apparatus that displays a left-eye image and a right-eye image on a display unit so as to be stereoscopically viewed,
Histogram creation means for creating respective histograms of the input left eye image and right eye image;
From the histogram of the image for the left eye created by the histogram creating means, a first dark portion feature amount that is a gradation value on a low gradation side and a first bright portion feature amount that is a gradation value on a high gradation side are detected. First detecting means for
From the histogram of the right-eye image created by the histogram creating means, the second dark portion feature amount that is the tone value on the low tone side and the second bright portion feature value that is the tone value on the high tone side are detected. Second detecting means for
An integrated dark portion feature amount is determined using the first dark portion feature amount and the second dark portion feature amount, and an integrated bright portion feature amount is determined using the first bright portion feature amount and the second bright portion feature amount. A determination means;
Setting means for setting a gradation correction parameter based on the integrated dark part feature and the integrated bright part feature;
An image processing apparatus comprising: correction means for correcting gradation of the left-eye image and right-eye image using the gradation correction parameter set by the setting means. The first detection means is a gradation value whose frequency is equal to or greater than a predetermined threshold and has a maximum value from the histogram of the left eye image, and whose local frequency variation is smaller than a predetermined reference. The target gradation that is the value is detected, the smallest gradation value among the attention gradations on the low gradation side is set as the first dark portion feature amount, and the largest among the attention gradations on the high gradation side Detecting a gradation value as the first bright portion feature amount;
The second detection means has a gradation value whose frequency is equal to or greater than a predetermined threshold and has a maximum value from the histogram of the right eye image, and whose local frequency fluctuation amount is smaller than a predetermined reference. The target gradation that is a value is detected, and the smallest gradation value among the attention gradations on the low gradation side is set as the second dark portion feature amount, and is the largest among the attention gradations on the high gradation side. The image processing apparatus according to claim 1, wherein a gradation value is detected as the second bright portion feature amount.   The determining means uses the minimum value of the first dark part feature value and the second dark part feature value as an integrated dark part feature value, and the maximum value of the first bright part feature value and the second bright part feature value. The image processing apparatus according to claim 1, wherein an integrated dark part feature amount is used.   The determining means uses an average value of the first dark part feature value and the second dark part feature value as an integrated dark part feature value, and uses an average value of the first bright part feature value and the second bright part feature value as an integrated dark part feature. The image processing apparatus according to claim 1, wherein the image processing apparatus is an amount.   5. The setting means sets a gradation correction parameter that enhances the gradation around the gradation value of the integrated dark portion feature amount and around the gradation value of the integrated bright portion feature amount. The image processing apparatus according to any one of the above. In an image processing apparatus that displays a left-eye image and a right-eye image on a display unit so as to be stereoscopically viewed,
Average luminance value detection means for dividing the input image for the left eye and the image for the right eye into a plurality of divided regions, and detecting an average luminance value in each divided region;
The minimum value and the maximum value among the average luminance values in each divided region of the image for the left eye are set as a first dark portion feature amount and a first bright portion feature amount, respectively, and the average luminance value in each divided region of the right eye image And determining the integrated dark part feature amount using the first dark part feature amount and the second dark part feature amount, with the minimum value and the maximum value of the second dark part feature amount and the second bright part feature amount as the second dark portion feature amount and the second bright portion feature amount, respectively. Determining means for determining an integrated bright part feature using one bright part feature and the second bright part feature;
Setting means for setting a gradation correction parameter based on the integrated dark part feature and the integrated bright part feature;
An image processing apparatus comprising: correction means for correcting gradation of the left-eye image and right-eye image using the gradation correction parameter set by the setting means.   The determination unit sets a smaller value of the first dark part feature value and the second dark part feature value as an integrated dark part feature value, and integrates a larger value of the first bright part feature value and the second bright part feature value. The image processing apparatus according to claim 6, wherein the image processing apparatus uses a bright portion feature amount.   Based on the difference between the average luminance value of each divided region of the left-eye image detected by the average luminance value detection means and the average luminance value of each divided region of the right-eye image, the left-eye image The image processing apparatus according to claim 6, further comprising an adjusting unit that adjusts an average luminance value of each of the divided areas and an average luminance value of each of the divided areas of the right-eye image.   9. The setting means sets a gradation correction parameter that enhances the gradation around the gradation value of the integrated dark portion feature amount and around the gradation value of the integrated bright portion feature amount. The image processing apparatus according to any one of the above. In the control method of the image processing apparatus for displaying the left-eye image and the right-eye image on the display unit so as to be stereoscopically viewed,
A histogram creating step of creating respective histograms of the input left eye image and right eye image;
From the histogram of the image for the left eye created in the histogram creation step, a first dark portion feature amount that is a gradation value on a low gradation side and a first bright portion feature amount that is a gradation value on a high gradation side are detected. A first detecting step to
From the histogram of the right-eye image created in the histogram creating step, the second dark portion feature amount that is the tone value on the low tone side and the second bright portion feature value that is the tone value on the high tone side are detected. A second detection step to
An integrated dark portion feature amount is determined using the first dark portion feature amount and the second dark portion feature amount, and an integrated bright portion feature amount is determined using the first bright portion feature amount and the second bright portion feature amount. A decision process;
A setting step for setting a gradation correction parameter based on the integrated dark portion feature amount and the integrated bright portion feature amount;
And a correction step of correcting the gradation of the left-eye image and the right-eye image using the gradation correction parameter set in the setting step. In the control method of the image processing apparatus for displaying the left-eye image and the right-eye image on the display unit so as to be stereoscopically viewed,
An average luminance value detecting step of dividing the input image for the left eye and the image for the right eye into a plurality of divided regions, and detecting an average luminance value in each divided region;
The minimum value and the maximum value among the average luminance values in each divided region of the image for the left eye are set as a first dark portion feature amount and a first bright portion feature amount, respectively, and the average luminance value in each divided region of the right eye image And determining the integrated dark part feature amount using the first dark part feature amount and the second dark part feature amount, with the minimum value and the maximum value of the second dark part feature amount and the second bright part feature amount as the second dark portion feature amount and the second bright portion feature amount, respectively. A determining step of determining an integrated bright part feature using one bright part feature and the second bright part feature;
A setting step for setting a gradation correction parameter based on the integrated dark portion feature amount and the integrated bright portion feature amount;
And a correction step of correcting the gradation of the left-eye image and the right-eye image using the gradation correction parameter set in the setting step.

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