KR101470833B1 - Apparatus for enhancing the foggy luminance image using lower bound of transmission rate - Google Patents

Abstract

The present invention relates to a device for improving a fog image using a lower transfer rate, and more particularly, to a device for improving the image quality of a fog, a smoke, a cloud and the like, in which the brightness and color components of the air are mixed with the light and color components of the object, So that a clear image can be provided.
In order to achieve the above object, according to the present invention, there is provided a transmission rate calculation apparatus comprising: a transmission rate calculation unit for receiving a fogged input image, calculating a transmission rate lower limit and a transmission rate estimation value, calculating a transmission rate by weighting the calculated transmission rate estimation value and a lower limit value; A reconstructed image calculation unit that receives the calculated transmission rate and the fogged input image through the transmission rate calculation unit, and outputs a fog removed image using the input transmission rate, the fogged input image, and the ambient brightness value; And a post-processing unit for performing a brightness enhancement process on the image output through the reconstructed image calculation unit and finally outputting a fog removed image; .

Description

FIELD OF THE INVENTION [0001] The present invention relates to a foggy image enhancement apparatus using a lower limit of transmission rate,

The present invention relates to a device for improving visibility of an image by removing fog or smoke from a luminance image whose image quality has been degraded due to fog or smoke, and more particularly, The present invention relates to a fog image improving apparatus using a lower limit of the transfer rate which can solve the problem that the performance is deteriorated and the backlight effect occurs due to the use of the filter and the difficulty of real time processing due to a demand for a large amount of calculation.

In recent years, video surveillance systems and vehicle image black boxes have been used to prevent or detect accidents. In the case of advanced safety vehicles, studies are underway to detect lane and forward vehicles from video images obtained by video camera using computer vision technology, and provide lane departure and vehicle collision alerting.

Clear input images are needed to improve the performance of image processing or computer vision application systems. Especially, when the object is detected / tracked or the edge information of the image is used, the better the input image is, the better the result can be obtained.

However, in the case of an image taken in an outdoor environment, the brightness and color reflected from the object are mixed with the particles in the air, so that the original bright color and brightness contrast can not be provided. Especially when there are large particles in the air, such as fog or smoke, it is difficult to accurately obtain the original color and shape of the object.

Related to the method of improving the image including the conventional fog, there have been many applications and disclosures in addition to Korean Patent Laid-Open No. 10-2010-0021952 (hereinafter referred to as 'prior art').

The method according to the preceding document includes the steps of generating a standby scattered light map based on a ratio of an average and a standard deviation of the first luminance image to a first luminance image of an image including atmospheric air light; And outputting a second luminance image from which the atmospheric scattering light is removed by subtracting the generated atmospheric scattering light map from the first luminance image; . However, the preceding document does not perform fog image processing on a pixel-by-pixel basis, but performs processing on a block-by-block basis.

In the meantime, various methods for estimating a clear image by removing fog from a foggy image have been proposed. In the beginning, in order to restore a foggy image to a foggy image, several images are used or additional information A method of using the same has been proposed. A method using polarized light [1, 2] has been proposed using multiple images, which acquires two or more images taken at exactly the same position with different polarizing filters mounted, Through the measurement method, the depth value was calculated and the fog was removed by using it.

Although these methods provide very good result images, there is a strong constraint that different polarizing filters should be used at the same location. The method of using multiple images without using a polarizing filter [3, 4, 5] is to remove the fog by obtaining fog value and depth information from two images of different weather conditions taken at the same position. Kopf et al. [6] proposed a method to remove fog using image depth information instead of using multiple images. We obtained depth or texture information using GPS information embedded in the camera, Density) is the depth information.

The methods of removing fog by using additional information using multiple images or a single image have a disadvantage that they can not be applied to images captured by dynamically moving cameras because it is necessary to acquire image data under various conditions have. Recently, a method of removing fog by a single image has been studied.

Tan proposed a method to remove fog by increasing the contrast of brightness in [8]. The clean image without fog has a higher edge strength than the fog image, and the fog value does not change rapidly The fog was removed. This method has the advantage that the shape and structure of the image are clearly recognized because the brightness contrast is greatly improved. However, the saturation phenomenon occurs due to the excessive contrast increase, and the halo effect occurs in the section where the depth information is much different It is also said. Fattal [9] proposed a method to reconstruct fog removed images by measuring the reflectance of the image through the assumption that the reflectance measured in the constant image region always has the same vector direction.

He et al. [10] used a characteristic that a clean image without fog has higher color saturation than a fog image. A pixel with high color sharpness of a fog free image has one channel of R, G, B channel) values are very small, we proposed a method to remove fog by using the observation result that there is a pixel with a very low channel value in a certain region in a foggy color image. However, since the conventional method using one image uses the RGB color, the fog removal performance is degraded when only the luminance image is used, the halo effect is exhibited due to the use of the large size filter, and a very large There is a problem that a computation amount is required and real-time processing is difficult.

Tarel et al. [11] proposed a fog removal method that uses a median filter to improve the computation speed. However, when a large-sized median filter is used, the operation speed is slowed and a halo effect appears.

[1] Y. Y. Schechner, S. G. Narasimhan, and S. K. Nayar, "Instant dehazing of images using polarization," in Proc. CVPR, pp. 1984-1991, Hawaii, USA, Dec. 2001.

[2] S. Shwartz, E. Namer, and Y. Schechner, "Blind haze separation," in Proc. CVPR, pp. 1984-1991, New York, USA, Oct. 2006.

[3] S.G. Narasimhan and S. K. Nayar, "Chromatic framework for vision in bad weather," in Proc. CVPR, pp. 598-605, SC, USA, June 2000.

[4] S. G. Narasimhan and S. K. Nayar, "Contrast restoration of weather degraded images," IEEE Trans. Pattern Anal. Mach. Intell., Vol. 25, no. 6, pp. 713-724, June 2003.

[5] S. K. Nayar and S. G. Narasimhan, "Vision in bad weather," in Proc. ICCV, pp. 820-827, Corfu, Greece, Sep. 1999.

[6] Deep photo: Model-based photographic enhancement and viewing, J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, ACM Trans. Graphics, vol. 27, no. 5, pp. 1-10, Dec. 2008.

[7] S. G. Narasimhan and S. K. Nayar, "Interactive deweathering of an image using physical models," In Workshop on Color and Photometry Methods in Computer Vision, OCT. 2003

[8] R.Tan, "Visibility in bad weather from a single image," in Proc CVPR, pp. 1-8, Alaska, USA, June 2008.

[9] R. Fattal, "Single image dehazing," ACM Trans. Graphics, vol. 27, no. 3, pp. 1-9, Aug. 2008.

[10] K. He, J. Sun, and X.Tang, "Single image haze removal using dark channel prior," Proc. CVPR, pp. 1956-1963, Miami, USA, June 2009.

[11] Tarel, Jean-Philippe; Hautiere, Nicolas; "Fast visibility restoration from a single color or gray level image," Computer Vision, 2009 IEEE 12th International Conference on, pp. 2201-2208, Sept. 2009.

Disclosure of the Invention The present invention has been made in view of the above problems, and it is an object of the present invention to provide an image processing apparatus and a method of processing the image, in which brightness and color components of air such as fog, smoke, and clouds are mixed with light and color components of an object, So that a clear image can be provided.

The existing fog removal method uses R, G, and B components of the input image, and performance degradation occurs when only the luminance component is used. However, since the color coordinate system used in most multimedia systems currently uses a luminance signal and a color difference signal like the YCbCr color coordinate system instead of the RGB coordinate system, a color coordinate system conversion process is required to apply the existing method. Color changes can occur when performing independent processing on each signal component. In addition, the existing fog removal method uses a large size filter, so that a halo effect occurs, and a very large amount of computation is required, so that real-time processing is difficult.

In the present invention, in order to solve a problem occurring in the conventional method, a lower limit of a transmission rate indicating how much fog is mixed with an original image in pixel units from an input image is calculated, and the lower limit of the calculated transmission rate And the fog removed image is obtained by using the estimated transfer rate. The method proposed in the present invention solves the problem of halo effect and high computation amount which appear in the conventional method because only the pixel unit operation is performed without using a filter.

In order to accomplish the above object, the present invention provides a foggy image enhancement apparatus using a lower transfer rate, which comprises a foggy input image, calculates a transfer rate lower limit value and a transfer rate estimate, calculates a transfer rate by weighting the calculated transfer rate estimate and a lower limit value A transfer rate calculation unit A reconstructed image calculation unit that receives the calculated transmission rate and the fogged input image through the transmission rate calculation unit, and outputs a fog removed image using the input transmission rate, the fogged input image, and the ambient brightness value; And a post-processing unit for performing a brightness enhancement process on the image output through the reconstructed image calculation unit and finally outputting a fog removed image; .

According to the present invention, brightness and color components in the air are reduced by blending the brightness and color components of the air such as fog, smoke, and clouds with the light and color components of the object, thereby providing a clear image There is an effect that can be done.

Since the color coordinate system used in most multimedia systems does not use the RGB coordinate system but uses the luminance signal and the color difference signal as in the YCbCr color coordinate system, the proposed method exhibiting excellent performance with only luminance information is applied to the multimedia system without conversion of the color coordinate system And the color of the input color can be maintained. In particular, since the proposed method performs pixel-based operation, it can solve the halo effect and the high computational cost problem of the conventional method using a large-sized filter, and thus can be applied to real-time applications.

Therefore, it is possible to solve the problem that visibility is reduced due to fog or smoke when applied to a high-definition surveillance system, a vehicle image black box, a fire prevention system, and the like, and can be applied to advanced safety vehicles that have recently been subjected to much research.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block diagram of an apparatus for improving a foggy image using a transfer rate lower limit according to the present invention; FIG.
2 is a configuration diagram of a mist image improving apparatus using a lower transfer rate lower limit according to the first embodiment of the present invention.
3 is a block diagram of a mist image improving apparatus using a lower transfer rate limit according to a second embodiment of the present invention.

Specific features and advantages of the present invention will become more apparent from the following detailed description based on the accompanying drawings. It is to be noted that the detailed description of known functions and constructions related to the present invention is omitted when it is determined that the gist of the present invention may be unnecessarily blurred.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will now be described in detail with reference to the accompanying drawings.

A mist image enhancing apparatus using a lower transfer rate according to the present invention will be described with reference to FIGS. 1 to 3. FIG.

The methods of removing fog by using one image commonly use the following fog modeling equation.

[Equation 1]

는 카메라를 통해 획득된 안개낀 영상의 x번째 화소값이고,

는 안개가 제거된 깨끗한 영상,

는 영상내의 화소 중 카메라에서 가장 먼 대기의 밝기값(atmospheric brightness)이다.

는 전달률(transmission rate)로서, 일반적으로 전달률

는 다음의 [수식 2] 와 같이 거리에 따라 지수함수적으로 감소한다.here,

Is the x-th pixel value of the fogged image acquired through the camera,

A clean image with fog removed,

Is the atmospheric brightness of the pixels in the image that is farthest from the camera.

Is the transmission rate and is generally expressed as the transmission rate

Is exponentially decreasing with distance as shown in the following [Equation 2].

[Equation 2]

는 공기의 산란계수이고,

는 x번째 화소에 대응하는 공간상의 점과 카메라 사이의 거리이다. here,

Is the scattering coefficient of air,

Is the distance between the point on the space corresponding to the x-th pixel and the camera.

가 되고, 매우 가까운 곳의 화소는 전달률이 1에 근접하므로

가 된다. 따라서, 영상에서 밝은 화소는 거리가 멀어 안개가 많이 낀 경우로 가정할 수 있고 전달률이 작다고 가정할 수 있다.Therefore, when the fog is constantly fixed, the transmission rate is close to zero at a distance such as the sky, so that [Equation 1]

, And the pixel near the pixel is close to 1

. Therefore, it can be assumed that the bright pixels in the image are assumed to be far away from the fog, and the transmission rate is small.

로부터

와

를 구하고, 이를 이용하여 최종적으로 안개가 제거된

를 복원하는 것이다. 상기 [수식 1] 로부터 전달률과 복원값은 각각 [수식 3] 과 [수식 4] 로 구할 수 있다.The fog removal is based on the input image

from

Wow

And finally, the fog removed

. The transfer rate and the restoration value can be obtained from [Expression 1] by [Expression 3] and [Expression 4], respectively.

[Equation 3]

[Equation 4]

는

를 만족해야하므로, [수식 4] 로부터 전달률

의 범위는 [수식 5] 와 같이 결정된다. 전달률 하한치는 짙은 안개로 인해 객체가 보이지 않거나 원래 객체의 밝기(radiance)가 없는 경우의 전달률이다. On the other hand,

The

Therefore, from Equation 4,

Is determined as shown in [Equation 5]. The lower limit of the transmission rate is the transmission rate when the object is not visible due to dense fog, or the original object has no radiance.

[Equation 5]

는 다음의 [수식 6] 과 같이 안개영상

의 전달률의 하한치

를 안개가 제거된 영상

의 전달률의 하한치

로 나눈값과 같다. [Formula 3] is rearranged,

As shown in the following Equation 6,

The lower limit of the transfer rate

Fog removed video

The lower limit of the transfer rate

.

[Equation 6]

라고 가정하고 [수식 4] 를 이용하여 추정한 안개가 제거된 영상

은 다음의 [수식 7] 과 같고,

인 경우 상기 [수식 3] 을 이용하여 추정한 전달률은 다음의 [수식 8] 과 같다.Delivery rate

And the fog-free image estimated using [Equation 4]

Is expressed by Equation (7) below,

, The transmission rate estimated using the above-mentioned [Equation 3] is expressed by the following [Equation 8].

[Equation 7]

[Equation 8]

인 경우 안개가 제거된 영상은

가 된다.Finally,

The fog removed image

.

In order to improve the fog removal effect, it is necessary to ensure that the transmissivity corresponding to the foggy pixels has a smaller value relative to the transmissivity of the foggy pixel.

와 전달률 하한치와의 가중합으로 계산하는데, 가중치가 추정된 전달률

로 설정하면 안개제거 효과를 증대시킬 수 있다. To this end, in the present invention, the transmission rate is calculated as a transmission rate estimate

And the lower limit of the transfer rate. The weighted transfer rate

The effect of removing fog can be increased.

[Equation 9]

인 경우 전달률과 안개가 제거된 영상은 각각 다음의 [수식 10] 과 [수식 11] 로 표현된다.Weight

, The images with transmission rate and fog removed are expressed by [Equation 10] and [Equation 11], respectively.

[Equation 10]

[Equation 11]

FIG. 1 is a block diagram of an apparatus for improving a fog using an upper limit of a transfer rate according to the present invention. The apparatus includes a transfer rate calculation unit 100, a reconstruction image calculation unit 200, and a post-processing unit 300 .

)을 입력받아 전달률 하한치(

) 및 전달률 추정치(

) 를 계산하고, 계산된 전달률 추정치와 하한치를 가중합하여 전달률(

)을 계산하는 기능을 수행하는 바, 상기 도 1 에 도시된 바와 같이 전달률 하한치 계산모듈(110), 전달률 추정모듈(120) 및 가중합 계산모듈(130)을 포함한다. The transfer rate calculation unit 100 calculates the transfer rate

) And the lower limit of transmission rate

) And transfer rate estimates

), And weights the computed transfer rate estimate and the lower bound to obtain the transfer rate

As shown in FIG. 1, the transfer rate lower limit calculation module 110, the transfer rate estimation module 120, and the weighted sum calculation module 130 are included.

)을 입력받아, [수식 5] 와 같은 화소단위로 안개낀 입력영상(

)에 대한 전달률 하한치(

)를 계산한다. 여기서, 대기의 밝기값

는 영상에서 가장 밝은 값으로 설정한다.Specifically, the transfer rate lower limit value calculation module 110 calculates the transfer rate lower limit value

) And receives the input image fogged in pixel units as shown in [Equation 5]

) Lower limit of transmission rate (

). Here, the brightness value of the atmosphere

Is set to the brightest value in the image.

)을 입력받아, [수식 8] 을 통해 전달률 추정치(

)를 계산한다. 이때, 전달률 추정치(

) 는 전달률 하한치를 제곱근한 값이다. The transfer rate estimation module 120 calculates the transfer rate

), And the transmission rate estimate (

). At this time,

) Is the square root of the lower limit of the transmission rate.

)와 상기 전달률 하한치 계산모듈(110)을 통해 계산된 전달률 하한치(

)를 [수식 9] 를 통해 가중합하여 안개가 제거된 영상의 복원에 사용될 전달률(

)을 계산한다.
The weighted sum calculation module 130 calculates a weighted sum calculation value

) And the transfer rate lower limit value calculation module 110

) Is multiplied by [Equation 9] to obtain the transmission rate to be used for reconstructing the fog removed image

).

)과 안개낀 입력영상(

)을 입력받아, 입력된 상기 전달률(

), 안개낀 입력영상(

) 및 대기의 밝기값(

)을 이용한 [수식 4] 를 통해 안개가 제거된 영상(

)을 출력한다.
The reconstructed image calculation unit 200 calculates the reconstructed image using the transfer rate calculated through the transfer rate calculation unit 100

) And fogged input image

) And receives the input transmission rate (

), Fogged input image (

) And the brightness value of the atmosphere

), The fog-free image (

).

The post-processing unit 300 performs processing such as brightness stretching on the image output through the reconstructed image calculation unit 200, and finally outputs a fog-free image.

FIG. 2 is a block diagram of a foggy image improving apparatus according to the first embodiment of the present invention. As shown in FIG. 2, the atmospheric brightness value calculating unit 100, the restored image calculating unit 200, .

로 사용하는 경우 안개가 제거된 영상은 상기 [수식 7] 과 같다. In the present embodiment, the fog removed image is represented by a very simple formula, and the transmission rate is calculated as an estimate of the transmission rate

The image with the fog removed is shown in Equation (7).

)을 입력받아, 대기의 가장 밝은 화소값(

)을 출력한다. The atmospheric brightness value calculation unit 100 calculates the atmospheric brightness value

) And receives the brightest pixel value (

).

)과 안개낀 입력영상(

)을 입력받아, 나눗셈 연산자(210), 뺄셈연산자(220), 제곱근연산자(230) 및 뺄셈연산자(240)를 통해

을 출력하고, 곱셈연산자(250)를 통해 상기 대기 밝기값 계산부(100)의 화소값과 뺄셈연산자(240)의 출력을 곱하여 안개가 제거된 영상(

) 을 출력한다.The restored image calculation unit 200 calculates the restored image by using the atmospheric brightness value calculated by the atmospheric brightness value calculation unit 100

) And fogged input image

), A division operator 210, a subtraction operator 220, a square root operator 230, and a subtraction operator 240

And multiplies the pixel value of the atmospheric brightness value calculation unit 100 and the output of the subtraction operator 240 through the multiplication operator 250 to output the fog removed image

).

)을 출력한다.
The post-processing unit 300 performs processing such as brightness stretching on the image output through the reconstructed image calculation unit 200, and finally outputs the fog removed image ((

).

FIG. 3 is a block diagram of an apparatus for improving a fog image using a transfer rate lower limit according to the second embodiment of the present invention. As shown in FIG. 3, the apparatus includes an atmospheric brightness value calculator 100, And a processing unit (300).

)을 입력받아, 대기의 가장 밝은 화소값(

)을 출력한다. The atmospheric brightness value calculation unit 100 calculates the atmospheric brightness value

) And receives the brightest pixel value (

).

)과 안개낀 입력영상(

)을 입력받아, 나눗셈 연산자(210), 뺄셈연산자(220), 제곱근연산자(230), 뺄셈연산자(240) 및 곱셈연산자(250)를 통해

을 출력하고, 덧셈연산자(260)를 통해 뺄셈연산자(240)의 출력에 상수 1을 더하여

을 구한 후, 나눗셈연산자(270)를 통해 곱셈연산자(250)의 출력을 덧셈연산자(260)의 출력으로 나누어, 안개가 제거된 영상을 출력한다.The restored image calculation unit 200 calculates the restored image by using the atmospheric brightness value calculated by the atmospheric brightness value calculation unit 100

) And fogged input image

The subtraction operator 220, the square root operator 230, the subtraction operator 240, and the multiplication operator 250. The division operator 210, the subtraction operator 220, the square root operator 230,

And a constant 1 is added to the output of the subtraction operator 240 through the addition operator 260

And divides the output of the multiplication operator 250 by the output of the addition operator 260 through the division operator 270 to output the fog removed image.

)을 출력한다.
The post-processing unit 300 performs processing such as brightness stretching on the image output through the reconstructed image calculation unit 200, and finally outputs the fog removed image ((

).

While the present invention has been particularly shown and described with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. It will be appreciated by those skilled in the art that numerous changes and modifications may be made without departing from the invention. Accordingly, all such appropriate modifications and changes, and equivalents thereof, should be regarded as within the scope of the present invention.

100: transfer rate calculation unit 200: reconstruction image calculation unit
300: post-processor 110: transmission rate lower limit calculation module
120: transfer rate estimation module 130: weighted sum calculation module

Claims (9)

delete Fogged input image (

) And the lower limit of transmission rate

) And transfer rate estimates

), And weights the computed transfer rate estimate and the lower bound to obtain the transfer rate

A transmission rate calculation unit 100 for calculating a transmission rate;
The transfer rate calculated by the transfer rate calculation unit 100

) And fogged input image

) And receives the input transmission rate (

), Fogged input image (

) And the brightness value of the atmosphere

) To remove the fog image

A reconstructed image calculation unit 200 for outputting reconstructed images; And
A post-processing unit (300) for performing brightness enhancement processing on the image output through the reconstructed image calculation unit (200) and finally outputting a fog removed image; , ≪ / RTI &
The restored image calculation unit 200 calculates a restored image,
The fog-free image (Fig. 4)

And outputting a lower limit of the transfer rate.
[Equation 4]

3. The method of claim 2,
The brightness value of the atmosphere (

)silver,
Wherein the fog image is the brightest value in the image.
3. The method of claim 2,
The transfer rate calculation unit (100)
Fogged input image (

), The input image fogged in units of pixels (

) Lower limit of transmission rate (

A transfer rate lower limit calculation module 110 for calculating a transfer rate lower limit value;
The fogged input image (

), Square root of the transmission rate estimate (

A transfer rate estimation module 120 for calculating a transfer rate; And
The transfer rate estimation module 120 calculates a transfer rate estimate

) And the transfer rate lower limit value calculation module 110

) Is weighted and the transmission rate

A weighted sum calculation module 130 for calculating a weighted sum; And a fog correction unit that corrects the fog image based on the lower limit of the transfer rate.
5. The method of claim 4,
The transfer rate

),

And a fog image enhancing device using the lower limit of the transfer rate.
delete 3. The method of claim 2,
The restored image calculation unit 200 calculates a restored image,
Delivery rate

, The fog removed image estimated through the above-mentioned [Expression 4] (

) Is calculated through the following equation (7): " (7) "
[Equation 7]

Fogged input image (

) And receives the brightest pixel value (

An ambient brightness value calculation unit 100 for outputting an ambient brightness value;
The brightest pixel value of the atmosphere calculated through the atmospheric brightness value calculation unit 100

) And fogged input image

), A division operator 210, a subtraction operator 220, a square root operator 230, and a subtraction operator 240

And multiplies the pixel value of the atmospheric brightness value calculation unit 100 and the output of the subtraction operator 240 through the multiplication operator 250 to output the fog removed image

A reconstructed image calculation unit 200 for outputting reconstructed images; And
The image output through the reconstructed image calculation unit 200 is subjected to a brightness stretching process, and finally, a fog removed image ((

A post-processing unit 300 for outputting a post-processing result; And a lower limit of the transmission rate.
Fogged input image (

) And receives the brightest pixel value (

An ambient brightness value calculation unit 100 for outputting an ambient brightness value;
The brightest pixel value of the atmosphere calculated through the atmospheric brightness value calculation unit 100

) And fogged input image

The subtraction operator 220, the square root operator 230, the subtraction operator 240, and the multiplication operator 250. The division operator 210, the subtraction operator 220, the square root operator 230,

And a constant 1 is added to the output of the subtraction operator 240 through the addition operator 260

A reconstructed image calculation unit 200 for dividing the output of the multiplication operator 250 by an output of the addition operator 260 through a division operator 270 and outputting a fog removed image; And
The image output through the reconstructed image calculation unit 200 is subjected to a brightness stretching process, and finally, a fog removed image ((

A post-processing unit 300 for outputting a post-processing result; And a lower limit of the transmission rate.

Images