KR20110139020A - Fatigue evaluation method and apparatus for 3d video based on depth map - Google Patents

Abstract

PURPOSE: A method for measuring the fatigue of a 3D image based on a depth image is provided to quantify the fatigue of a 3D image which is comprised of a 2D image and a depth image. CONSTITUTION: A parameter calculation unit receives a 3D image which is comprised of 2D image and a depth image. By using a pixel value of the depth image, the parameter calculation unit calculates each parameter expressing the spatial complexity, the depth generation location, the temporal complexity, and the average motion speed of a 3D image(210). A fatigue calculation unit calculates the fatigue of 3D image which is inputted through the calculated each parameter(220).

Description

Fatigue evaluation method and apparatus for 3D video based on depth map}

The present invention relates to a three-dimensional imaging technology, and more particularly, to a method and apparatus for measuring fatigue of a three-dimensional image based on a depth image.

3D image technology is a representative technology of immersive broadcasting that follows digital TV broadcasting. 3D imaging is a technology that uses the binocular vision principle in which humans recognize stereoscopic space using two eyes, and many experts research and various countries are focusing on technology development. However, unlike two-dimensional images, three-dimensional images have a lot of human factors, which makes them difficult to develop. Although 3D image processing technology and 3D display are being researched and developed at high speed, the development speed of the 3D image market is less than expected due to the fatigue caused by the 3D image. 3D images cause fatigue and dizziness due to mismatches in focal length and convergence angle, differences in images provided to both eyes, and excessive time and space changes, which are directly related to viewer safety. In particular, due to incomplete technology, the interest in safety has increased after the photosensitive seizure in Japan. Therefore, the development of 3D image evaluation technology that can guarantee safety by considering the effect on the eyes as a top priority has emerged as a very important issue, and the standardization of image quality evaluation related to safety is also active in Korea and abroad.

In the 3D safety evaluation research, the subjective image quality evaluation is mainly made according to the 3D image characteristics and the type of display. However, most of the fatigue measurement of 3D image is limited to subjective evaluation, and much research on the safety of 3D image is still insufficient.

An object of the present invention is to provide a method and apparatus for measuring fatigue of a 3D image capable of quantifying the fatigue of a 3D image including a 2D image and a depth image.

In order to solve the above technical problem, according to the present invention, a fatigue measurement method of a three-dimensional image consisting of a two-dimensional image and a depth image, the spatial complexity of the three-dimensional image, depth generating position, Calculating each parameter indicative of temporal complexity, average motion velocity; And calculating the fatigue degree of the 3D image using the calculated respective parameters.

The parameter representing the spatial complexity may be calculated using a variance of pixel values of the depth image of each frame.

In addition, in obtaining a variance of pixel values of the depth image, the variance may be obtained by dividing the depth image by regions and assigning different weights to the regions.

In addition, in calculating the parameter representing the spatial complexity, the parameter may be normalized to a value between 0 and 1 using the variance of the pixel value of the depth image of each frame as an exponent of the exponential function.

The parameter representing the depth generation position may be calculated by using a difference between a mean of pixel values of the depth image and a position of the 3D display at a depth level.

The parameter representing the temporal complexity may be calculated using a variance of pixel value differences of depth images between successive frames.

In addition, in obtaining the variance of the pixel value difference of the depth image between the successive frames, the variance may be obtained by dividing the depth image for each region and giving different weights for each region.

The parameter representing the average movement speed may be calculated using an average of pixel value differences of depth images between successive frames.

In calculating the fatigue degree of the 3D image, the fatigue degree may be calculated by a linear combination of the calculated respective parameters.

In order to solve the above technical problem, there is provided a computer-readable recording medium recording a program for executing the above-described fatigue measurement method of the three-dimensional image.

In order to solve the above technical problem, according to the present invention, a fatigue measurement apparatus of a three-dimensional image consisting of a two-dimensional image and a depth image, the spatial complexity of the three-dimensional image, depth generating position, A parameter calculator for calculating each parameter representing a temporal complexity and an average motion speed; And a fatigue calculator for calculating fatigue of the 3D image using the calculated respective parameters.

According to the present invention described above, a method and apparatus for measuring fatigue of a 3D image, which can quantify the fatigue of a 3D image including a 2D image and a depth image, are provided.

1 is an example of a 2D plus depth image.
2A is a flowchart of a method of measuring fatigue of a 3D image according to an exemplary embodiment.
2B is a block diagram of an apparatus for measuring fatigue of a 3D image according to an exemplary embodiment.
3 illustrates an example of weights for respective zones of a depth image.
4 shows a conceptual diagram of the comfort region.
5 is an example of a depth image of an image having a large spatial complexity and a small image.
6 is an example of a depth image having different depth generation positions.
7 is an example of an experimental image used.
8 illustrates fatigue and subjective fatigue measured according to an embodiment of the present invention.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the following description and the accompanying drawings, substantially the same components are denoted by the same reference numerals, and redundant description will be omitted. In addition, in the following description of the present invention, if it is determined that a detailed description of a related known function or configuration may unnecessarily obscure the subject matter of the present invention, the detailed description thereof will be omitted.

According to an embodiment of the present invention, for example, a fatigue degree of a 2D plus depth image of an autostereoscopic multiview display as shown in FIG. 1, that is, a 3D image composed of a 2D image and a depth image is calculated. The reason why the non-glass multiview display is used instead of the stereoscopic display is firstly because of limitation of viewing area and inconvenience of wearing glasses. Generally, TV is regarded as a device that can be viewed regardless of the viewing area, but in the case of 3D TV, because it provides different images in both eyes to feel 3D, it is difficult to provide images to a large number of viewers, and stereo TV uses a polarization method. Because of the inconvenience to carry and wear glasses. Second, it is likely to be applied to next-generation realistic broadcasting. Accurately measuring disparity or depth in stereo images is very complex and time consuming. However, since 2D plus depth images already contain depth information, they can be effectively applied to technologies such as free-view TV. MPEG (Depth-Map Based Rendering) techniques can be used to generate color images at different times.

The fatigue of the 3D image is due to the spatiotemporal characteristics of the image such as the screen composition and various fast movements of the objects. Therefore, according to an embodiment of the present invention, the fatigue degree is measured by measuring it in the depth image.

2A is a flowchart of a method of measuring fatigue of a 3D image according to an exemplary embodiment of the present invention, and FIG. 2B is a block diagram of a fatigue measuring apparatus of a 3D image according to an embodiment of the present invention.

According to the present exemplary embodiment, the parameter calculator 1010 receives a 3D image including a 2D image and a depth image, and uses the pixel values of the depth image to determine the spatial complexity, depth generation position, temporal complexity, Each parameter representing the average movement speed is calculated (step 210).

The parameter calculator 1010 may be implemented as a processor for loading and processing data necessary for a task and a processor for data comparison and operation in a typical computing environment.

The fatigue calculator 1020 calculates the fatigue degree of the input 3D image by using the calculated respective parameters and outputs the calculated value (step 220).

The fatigue calculator 1020 may also be implemented as a processor for loading and processing data required for a task and a processor for data comparison and calculation in a typical computing environment.

Since the pixel value of the depth image reflects the structure and position of the object, direct measurement is possible, and unlike the color image, the pixel value is easy to adjust the value. According to an embodiment of the present invention, when the fatigue level of the image is measured, it may be used for safety monitoring and improvement of the 3D image.

According to an embodiment of the present invention, the depth image information of the 2D plus depth image is used to measure the fatigue degree by measuring the characteristics of the 3D image as described above. Compared to the color image, the depth image has different depth values depending on the object, and most of the depth images have the same depth value in the object. Therefore, it is possible to directly measure the motion and characteristics of the object because there is no need to separate the objects. .

In the exemplary embodiment of the present invention, in the case of a three-dimensional image without special noise and distortion, it is assumed that the two-dimensional color image does not have a large influence on fatigue, and fatigue is measured using only the spatiotemporal characteristics of the depth image. The spatial and temporal characteristics of the depth image to be measured and the physical meaning of each element are shown in Table 1 below.

The spatiotemporal characteristics of the depth image are determined by factors such as the number of objects, change in depth value, and speed of movement. Images with many fast moving objects present great difficulty in compositing depth. Therefore, in an embodiment of the present invention, the fatigue degree of the 3D image is measured by measuring the position where the depth of the image is generated on the display and the movement speed of the overall image in addition to the spatial / temporal complexity.

The calculation of a parameter representing the spatial complexity of the screen configuration of the 3D image will be described below.

If very many objects in a scene are distributed at multiple depth levels, it is not easy to synthesize depths at the same time. In an embodiment of the present invention, as shown in Equation 1, the depth distribution range of an object and a plurality of objects exist in a scene by using the variance of pixel values of the depth image of each frame.

In addition, since spatial complexity is a factor reflecting the characteristics of the screen configuration, the depth image may be classified for each region and given a different weight for each region to obtain a dispersion of pixel values of the depth image. For example, as shown in FIG. 3, the weight is divided into a portion where the line of sight is concentrated and a portion that is not, and a different weight is applied to the portion where the line of sight is concentrated. Referring to FIG. 3, the center portion where the gaze is mainly concentrated is 1.2, and the corner portion where the gaze is less concentrated is 0.85.

In one embodiment of the present invention, the parameter representing the final spatial complexity is the average of the variance of the pixel values of the depth image of each frame, as shown in Equation 1, and also using an exponential function, that is, the depth of each frame The variance of the pixel values of the image can be normalized to a value between 0 and 1 using the exponential of the exponential function.

Where C _spatial is a parameter representing the spatial complexity of the 3D image, N is the number of frames of the image, m, n is the size (horizontal and vertical) of the depth image, and (x, y) is the pixel coordinate of the depth image. Where w (x, y) is the weight of pixel (x, y), f _i (x, y) is the pixel value of pixel (x, y) of the i-th frame depth image, and E (f _i (x , y)) represents an average of pixel values of an i-th frame depth image. And [sigma] _s is a constant value arbitrarily selected as a scale factor. σ _s can be determined through experiments so that the spatial complexity parameter values of the N images are distributed evenly between 0 and 1. FIG.

The spatial complexity parameter described above has a value close to zero when a large number of objects exist in the depth image, the screen configuration is coarse, and the depth is generated over the entire area of the depth level, and the scene is simple and not complicated. If the number of depth levels is small, the value is close to one. This characteristic is a significant factor in the overall fatigue of the image.

Hereinafter, the calculation of the parameter indicating the depth generation position of the three-dimensional image will be described.

In the 3D image, as shown in FIG. 4, there is a comfortable range for the two eyes to synthesize the depth based on the display, which is called the comfort region. [3D Movie Making: Stereoscopic digital cinema from script] to screen, "Bernard Mendiburu, FOCAL PRESS, 2009." Outside of this comfort zone, both eyes are difficult to synthesize depth, causing fatigue. In particular, if the depth is distributed evenly over the entire depth level, the difference between the foreground and the rear view is large, and you may feel fatigued. Even if all the depths are distributed in front of or behind the display, it may appear to be projected forward or recessed as a whole. I feel it. Therefore, it is necessary to measure where the depth is generated, that is, how much the depth is formed in the comfort zone.

In one embodiment of the present invention, the depth generation position measurement is calculated by using the difference between the average of pixel values of the depth image and the position of the 3D display in the depth level, as shown in Equation 2.

Where A _spatial is the parameter representing the depth generation position, N is the number of frames of the image, D _display is the position of the three-dimensional display plane at the depth level (e.g. D _display = 128 for Philips WOWvx autostreoscopic 3D Display), (x, y) is the pixel coordinate of the depth image, f _i (x, y) is the pixel value of the pixel (x, y) of the i-th frame depth image, and E (f _i (x, y)) is i The average of the pixel values of the first frame depth image. _s is also a constant value chosen arbitrarily as a scale factor, like _s . The depth value is closer to zero, the rear of the display, and closer to 255 is the front of the display. As the image deviates from the comfort zone, it causes fatigue, so the A _spatial value is close to 0, and the closer to 1 the _spatial value is.

The calculation of the parameter representing the temporal complexity of the 3D image will be described below.

If there are multiple movements of various speeds on the screen, it is not easy to synthesize each object moving at different speeds at the same time. Therefore, in the present embodiment, as shown in Equation 3 below, by measuring a parameter representing the temporal complexity by using the variance of the pixel value difference of the depth image between successive frames, it is measured how the various speed movement in the screen. However, in order to not be affected by the spatial characteristics of the depth image in measuring such temporal characteristics, it is preferable to extend the range of the depth value to the whole area between 0 and 255 to unify and measure it.

Temporal complexity also reflects the structural characteristics in which the line of sight is centered in the middle of the screen, so as in the case of spatial complexity, as shown in FIG. Can be given more weight.

Where C _temporal is a parameter representing the temporal complexity of the 3D image, N is the number of frames of the image, m and n are the size (horizontal and vertical) of the depth image, and (x, y) is the pixel coordinate of the depth image. Where w (x, y) is the weight of pixel (x, y), and d _i (x, y) is the pixel value f _i (x, y) and i + 1 th frame depth image of the i-th frame depth image. Denotes the difference value of the pixel value f _{i + 1} (x, y). And σ _t is a constant value arbitrarily selected as a scale factor like σ _s .

In the image, when the overall change of the screen and many objects move at different speeds, the temporal complexity parameter is close to zero, and the smaller the movement of the object, the closer to one. This temporal complexity, like spatial complexity, is a major factor in measuring image fatigue. However, since temporal complexity can measure only the variety of motions and does not reflect the speed of the image, it is necessary to measure the overall average motion speed of the image to be described later.

Hereinafter, the calculation of the parameter indicative of the average motion speed of the three-dimensional image will be described.

If the image moves quickly over time, it will cause fatigue due to lack of time to synthesize depth. Therefore, as shown in Equation 4, the average motion speed of the entire image is measured by obtaining a parameter representing the average motion speed of the image using an average of pixel value differences of depth images between successive frames. At this time, the depth level range is measured to be extended from 0 to 255 as in the time complexity. If temporal complexity represents the variety of movements, then the average movement velocity represents the magnitude of the movement.

Here, A _temporal is a parameter representing the average moving speed of the 3D image, N is the number of frames of the image, (x, y) is the pixel coordinate of the depth image, w (x, y) is the pixel (x, y D _i (x, y) is the difference between the pixel value f _i (x, y) of the i-th frame depth image and the pixel value f _{i + 1} (x, y) of the i + 1 th frame depth image. Indicates a value. And ρt, like σ _s , is a constant value that is arbitrarily chosen as a scale factor.

The faster the overall speed of the image, the closer the average motion speed parameter is to 0, and the slower the value to be closer to 1.

When the four parameters described above are obtained, the fatigue calculator 1020 calculates the fatigue degree of the 3D image by a linear combination of these parameters. The fatigue degree C of the 3D image may be calculated according to Equation 5 below.

Here, α, β, γ, and δ are coefficients of a linear combination and are predetermined values.

In order to obtain a fatigue value that has a high correlation with the subjective evaluation, the least square method is used to find the optimum coefficients α, β, γ, and δ, and the fatigue values can be calculated using the found values. In this case, the following Equations 6 and 7 may be used.

Here, N denotes the number of subjective evaluation target 3D images, X denotes a 4′1 parameter vector having X ^T = (α β γ δ), and S denotes a subjective evaluation vector of N ㅧ 1.

According to one embodiment of the present invention, the fatigue measurement results have a value between 0 and 5 similarly to the subjective fatigue evaluation results, the results will be described later.

As already mentioned, incomplete 3D imaging is accompanied by harmful symptoms such as dizziness or headache. Therefore, it is necessary to minimize the fatigue level in the acquisition phase or display phase of the 3D image. Since the fatigue measurement method according to the exemplary embodiment of the present invention uses a 2D plus depth type image, direct measurement may be performed using the depth image.

There are three main factors that can cause fatigue in 3D image in 1 view 1 depth format. The first is a case of large spatial complexity. When a large number of objects exist in one screen, viewers may feel tired due to mismatches in focal length and convergence angle. For example, referring to FIG. 5, (a) has a larger number of objects on the screen and has a complicated screen configuration than (b), resulting in greater fatigue. When you focus on the depth of an object, objects formed at different depth levels appear superimposed or blurred. In this case, it may be regarded as an image having a small C _spatial value which is a spatial complexity parameter among fatigue measurement parameters according to an exemplary embodiment of the present invention.

The second case is large time complexity. When multiple objects have different speeds in one screen, their eyes are dispersed and objects with different speeds are difficult to synthesize depth at the same time. The movement of the edges or small objects, except for the middle part of the screen where the eyes are focused, and the large objects, is unnecessary and unobtrusive to recognize the depth. In addition, when the screen is changed quickly and the camera movement is dynamic, there is a lack of time to recognize the depth due to the sudden change of the screen, and the user may feel fatigue, and may not be able to feel three-dimensional, so it may look like a two-dimensional image. In this case, in the fatigue measurement method according to an embodiment of the present invention, the temporal complexity parameter C _temporal value is small and the average motion velocity parameter A _temporal is also an image having a small value.

The third is where the depth generation location is outside the comfort zone. The depth image has a gray level of 0 to 255. If the depth is evenly distributed at the previous level as shown in FIG. 6 (a), it is difficult to feel the depth at the same time because the difference between the convergence angles of the foreground and the rearview mirror is large. In addition, when the overall depth of the image is concentrated in the foreground or the rear view as shown in (b) of FIG. 6, the 3D image may not be stable due to the characteristics of the display. You will feel as if you are buoyed or immersed, and the deterioration of image quality will also appear, making you feel unnatural and tired. In this case, in the fatigue measurement method according to an embodiment of the present invention, the depth generating position parameter A _spatial may be an image showing a small value. FIG. 6C illustrates an image in which depth is generated in the comfort region based on the display.

In addition, even if the special characteristics such as the three causes described above are not measured, if the final fatigue measurement value is below the standard, it can be regarded as an image that causes fatigue. Therefore, by observing the results of the fatigue measurement parameters according to an embodiment of the present invention it is possible to measure the characteristics of the three-dimensional image, it is possible to detect the image causing the fatigue. This means that safety monitoring of three-dimensional images is possible, and that fatigue measurement, which previously relies only on subjective evaluation, is automatically possible.

Hereinafter, the experimental results of measuring the fatigue degree of the 3D image according to the fatigue degree measuring method according to an exemplary embodiment of the present invention will be described.

In this experiment, a 20-inch Philips WOWvx autostreoscopic 3D display (resolution 1600X1200) was used to measure the fatigue of the 3D image. Input file format is 2D plus depth image and inputs 2D color image and depth image of same size side by side.

The experimental images used 10 images provided by Philips, each showing different characteristics including the number of different objects and the size of the movement. 7 is an example of an experimental image used, (a) is an image of large spatial and temporal complexity (b) is an image of large spatial complexity and small temporal complexity, (c) is an image of small spatial complexity and large temporal complexity, (d) shows a small spatial and temporal complexity image.

In order to measure the fatigue degree according to the depth generation position, experiments were performed on a total of 30 images by scaling the range of depth values and adding an image set in which depth is generated only in front of the display and an image set located in the comfort region as shown in FIG. 6.

According to an embodiment of the present invention, when stiffness was measured, σ _s = 40, ρ _s = 40, σ _t = 10, ρ _t = 2 so that the measured value of each parameter in the entire experimental video was 0.5 on average. Coefficients obtained using Equations 6 and 7 are α = 1.7423, β = 1.8103, γ = 0.7304, and δ = 1.2379 so that fatigue results can be similarly observed.

Subjective evaluations were conducted on 8 males and 2 females with an average of 26.8 years of age without problems in viewing stereoscopic images. The running time of the images was 10 seconds and the vote was 2 seconds. In general, subjective quality evaluation uses the DSCQS (double stimulus continuous quality scale) method. In this experiment, there is no image to be compared, and an absolute category rating (ACR) method was selected to minimize the fatigue of the subject due to the shortening of time. See “” Subjective assessment of stereoscopic television pictures, ”ITU-R Recommendation BT.1438, 2000.]) Table 2 below shows fatigue assessment steps for subjective assessment.

8 illustrates fatigue and subjective fatigue measured according to an embodiment of the present invention. FIG. 8A illustrates subjective fatigue evaluation for each image set having a different depth generation range, and FIG. 8B illustrates fatigue measured according to an embodiment of the present invention. The analysis shows that each fatigue measurement element clearly distinguishes the characteristics of each image when compared with the subjective evaluation ( C _spatial). : F (4,45) = 4.82, p <0.01, A _spatial : F (4,45) = 3.60, p <0.05, C _temporal : F (4,35) = 8.66, p <0.01, A _temporal : F (4,45) = 6.52, p <0.01, where (F (m, n) = a, p <b) is the analysis of variance (ANOVA), where m and n are the degrees of freedom and a is the Value / variance in population), and p represents the significance level. It can be confirmed through subjective evaluation and fatigue measurement according to an embodiment of the present invention that the depth of the image existing in the comfort region is more comfortable than the image that is not. Referring to FIG. 8, it can be seen that most images having evenly distributed depths and low screen transitions show fatigue tendency similar to subjective evaluation according to characteristics of depth images.

According to the present invention described above, the fatigue degree can be measured only by the characteristics of the image, unlike the three-dimensional image fatigue test, which has been limited to the existing subjective evaluation. In particular, in the autostereoscopic display, the fatigue can be measured using only the characteristics of the depth image in the 2D plus depth format image. The spatial and temporal complexity of the depth image, the location of the depth generation, and the overall speed of the image are factors for measuring the characteristics of the image. The linear combination of the measured characteristic elements of the depth image can be used to measure fatigue with high similarity to subjective image quality. According to the present invention, it is also possible to objectively measure the fatigue and to monitor the fatigue of the three-dimensional image and ensure the safety.

Meanwhile, the above-described embodiments of the present invention can be written as a program that can be executed in a computer, and can be implemented in a general-purpose digital computer that operates the program using a computer-readable recording medium. The computer-readable recording medium may be a magnetic storage medium (for example, a ROM, a floppy disk, a hard disk, etc.), an optical reading medium (for example, a CD-ROM, DVD, etc.) and a carrier wave (for example, the Internet). Storage medium).

So far I looked at the center of the preferred embodiment for the present invention. Those skilled in the art will appreciate that the present invention can be implemented in a modified form without departing from the essential features of the present invention. Therefore, the disclosed embodiments should be considered in an illustrative rather than a restrictive sense. The scope of the present invention is shown in the claims rather than the foregoing description, and all differences within the scope will be construed as being included in the present invention.

Claims (19)

As a fatigue measurement method of a three-dimensional image consisting of a two-dimensional image and a depth image,
Calculating each parameter representing a spatial complexity, a depth generation position, a temporal complexity, and an average movement speed of the 3D image using pixel values of the depth image; And
And calculating the fatigue degree of the 3D image using the calculated respective parameters. The method of claim 1,
The parameter representing the spatial complexity is,
The fatigue measurement method characterized in that the calculation using the dispersion of the pixel value of the depth image of each frame. The method of claim 2,
The fatigue measurement method of claim 1, wherein the dispersion of pixel values of the depth image is obtained by dividing the depth image by regions and assigning different weights to regions. The method of claim 2,
In calculating the parameter representing the spatial complexity,
A fatigue measurement method, characterized in that the parameter is normalized to a value between 0 and 1 using the variance of the pixel value of the depth image of each frame as an exponential function. The method of claim 1,
The parameter representing the depth generation position is
The fatigue measurement method characterized in that the calculation using the difference between the position of the three-dimensional display in the average and depth level of the pixel value of the depth image. The method of claim 1,
The parameter representing the temporal complexity is
A fatigue measurement method, characterized in that it is calculated using the variance of the pixel value difference of the depth image between successive frames. The method of claim 6,
The fatigue measurement method of claim 1, wherein the dispersion of the pixel values of the depth images between the consecutive frames is obtained by dividing the depth images by regions and assigning different weights to the regions. The method of claim 1,
The parameter representing the average movement speed is
A fatigue measurement method characterized in that the calculation using the average of the pixel value difference of the depth image between successive frames. The method of claim 1,
In calculating the fatigue degree of the 3D image,
And calculating the fatigue degree by the linear combination of the calculated respective parameters. A computer-readable recording medium having recorded thereon a program for executing the method for measuring fatigue of a three-dimensional image according to any one of claims 1 to 9. A fatigue measurement device of a three-dimensional image consisting of a two-dimensional image and a depth image,
A parameter calculator configured to calculate each parameter representing a spatial complexity, a depth generation position, a temporal complexity, and an average motion speed of the 3D image by using pixel values of the depth image; And
And a fatigue calculation unit for calculating the fatigue degree of the 3D image by using the calculated respective parameters. The method of claim 11,
The parameter representing the spatial complexity is,
The fatigue measurement device, characterized in that the calculation using the dispersion of the pixel value of the depth image of each frame. The method of claim 12,
The fatigue measurement apparatus of claim 1, wherein the dispersion of pixel values of the depth image is obtained by dividing the depth image by regions and assigning different weights to regions. The method of claim 12,
In calculating the parameter representing the spatial complexity,
And a variance of pixel values of the depth image of each frame as an index of an exponential function to normalize to a value between 0 and 1. The method of claim 11,
The parameter representing the depth generation position is
The fatigue measurement apparatus, characterized in that calculated using the difference between the position of the three-dimensional display in the average and depth level of the pixel value of the depth image. The method of claim 11,
The parameter representing the temporal complexity is
A fatigue measurement device, characterized in that it is calculated using the variance of the pixel value difference of the depth image between successive frames. The method of claim 16,
The fatigue measurement apparatus of claim 1, wherein the dispersion of the pixel values of the depth images between the consecutive frames is obtained by dividing the depth images by regions and assigning different weights to the regions. The method of claim 11,
The parameter representing the average movement speed is
A fatigue measurement device, characterized in that it is calculated using the average of the pixel value difference of the depth image between successive frames. The method of claim 11,
In calculating the fatigue degree of the 3D image,
And calculating the fatigue degree by the linear combination of the calculated respective parameters.

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