Image contrast enhancement based on local brightness and contouring artifact improvement for large-scale LCD TVs

JONG-HEE HWANG1,2, JEAN Y. SONG1, YOON-SIK CHOE1

1Department of Electrical and Electronics Engineering, Yonsei University, Seoul, REPUBLIC of KOREA 2Preceding Development Division, LG Display Co., LTD, Paju, REPUBLIC of KOREA

jonghee38@yonsei.ac.kr, jeanyoung@yonsei.ac.kr, yschoe@yonsei.ac.kr

Abstract: - Generally, the global histogram equalization of contrast enhancement methods was used in various application fields because of its simple and effective function. But, as it has a disadvantage that the brightness of an image is changed excessively, in spite of heavy computational complexity, overlapped sub-block histogram equalization method reflecting local histogram properties is used mainly. Also, the global and local histogram equalization methods increase excessively the contrast of simple background occupying wide scope, and they causes the false contour which is unpleasant to the perception operation of an observer. So, in order to improve the local brightness, this paper derived the local histogram equalization (LHE) with the minimal overlapped block size that is optimized for Full High Definition (FHD: 1920×1080) representing the standard image size of large-size LCD (Liquid Crystal Display) TV and can be applied without causing the blocking artifacts, and then it proposed the image contrast enhancement method that combines the modified error diffusion (ED) method in order to reduce the contouring artifacts. This experimental results show that the proposed contrast enhancement method preserves the local image brightness and suppresses the contouring artifacts. Key-Words: - Histogram equalization, Local brightness, Contouring artifacts, False contour, Sub-block overlap.

1 Introduction Contrast enhancement is an image process technique of a low step that it does clearly an area of interest at images or redistributes intensity values to improve image quality. So, it clarifies the visual difference between dark and bright areas of an image. If the contrast of an image is increased, an observer can see a pictorial image in more detail. This is the pure perception operation that the gross amount of information never increases at images. Our perception operation is more sensitive to the contrast of brightness rather than the intensity of a pure brightness.

The most popular of these methods is called histogram equalization. Histogram equalization reassigns the brightness values of pixels based on the image histogram. Individual pixels retain their brightness order (that is, they remain brighter or darker than other pixels) but the values are shifted, so that an equal number of pixels have each possible brightness value. In many cases, this spreads out the values in regions where different regions meet, showing detail in areas with a high brightness gradient. An image having a few regions with very similar brightness values presents a histogram with peaks. The sizes of these peaks give the relative area of the different phase regions and are useful for image analysis [1].

Histogram equalization techniques can be classified greatly in ways to use global information and local information for input images. As global histogram

equalization doesn't take space information of each part at images into consideration and uses histogram information of the entire image, it is hard to improve a local contrast value. In addition, as it converts the average brightness of the image to the middle brightness value of the image by redistributing the brightness value of the image, it causes the phenomenon that bright areas of the

image becomes hazy [2∼3]. Local histogram equalization (LHE) technique has

been used primarily to overcome these problems with global contrast enhancement. A number of ways including AHE (Adaptive Histogram Equalization) [4], POSHE (Partially Overlapped Sub-block Histogram Equalization) [5], BBHE (Brightness preserving Bi-Histogram Equalization) [6], RSIHE (Recursive Sub-Image Histogram Equalization) [7] have been proposed. However, the various methods mentioned above must decide on the size of a partition block and the size of an overlapped step in common and need a lot of process time in proportion to the resolution of the image and the density of histogram. Also, as the variation of pixel values increases in the flat area that the brightness should change gradually, the false contour is generated.

So, in order to improve the local brightness, this paper derived the LHE with the minimal overlapped block size that is optimized for FHD representing the standard image size of large-size LCD TV and can be applied without causing the blocking artifacts, and then it

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proposed the image contrast enhancement method that combines the modified error diffusion (ED) method in order to improve the contouring artifacts.

The rest of this paper is organized as follows. Section 2 explains the previous research for contrast enhancement algorithm. Section 3 presents the error diffusion technique. Our proposed contrast enhancement method is present in Section 4. Section 5 describes the experimental results. Finally, we conclude in Section 6.

2 Histogram Equalization Overview This section summarizes fundamental background of

global histogram equalization (GHE) and block overlapped histogram equalization typically being used in local histogram equalization (LHE). 2.1 Global Histogram Equalization (GHE) If a given input image is defined by X={X(i,j) | X(i,j)∈{X0,X1,…,XL-1}, it can be consists of L brightness levels. X (i, j) represents the brightness of a given image in the spatial position. The probability density function for X is defined as equation (1).

1)(,10

)(

1

0

=≤≤

=

∑−

=

L

kkXk

kkX

XpX

n

nXp (1)

Here, k is the range from 0 to L-1, n is the total number of pixels of a input image, nk represents the number of occurrences of brightness value Xk.

Next, the cumulative distribution function for brightness value Xk like equation (2) can be obtained from the probability density function

∑=

=k

jjX Xpxc

0

)()( (2)

Here, k is the range from 1 to L-1, c(XL-1) is 1. As histogram equalization is a scheme that maps an input image into the entire dynamic area (X0, XL-1) using equation (2), it can be expressed as the conversion function f (x) of equation (3).

)()()( 010 xcXXXxf L −+= − (3)

Using GHE in order to enhance the global contrast,

GHE doesn’t reflect brightness characteristics of local areas as shown in Fig. 1. In addition, as it converts the average brightness of the image to the middle brightness value of the image by redistributing the brightness value of the image, it causes the phenomenon that bright areas of the image becomes hazy. Especially, including a lot of areas with similar brightness, the contrast can be reduced or image quality can be damaged because we can’t adjust

locally the contrast. These problems are solved by using local contrast enhancement technique.

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Fig.1 Contrast enhancement using global histogram equalization: (a) original image; (b) test results. 2.2 Block Overlapped Histogram Equalization The block overlapped histogram equalization is performed with the following procedures. First, a sub-block of appropriate size is set up. Second, the local histogram regarding the sub-block is obtained. Third, histogram equalization is performed by using this information in the central point of a relevant sub-block, and then the sub-block is moved by one pixel. In this way, as each pixel of an input image is redistributed by the local histogram of the neighboring sub-block, each point can be adapted in a brightness situation near this point and this method can increase the contrast of all objects and background. However, this method must perform the sub-block histogram equalization about all pixels of an input image. So, the number of iterations increases excessively.

In order to reduce the repetition frequency of block overlapped histogram equalization, the number of overlap should be reduced. For this purpose, non-overlapped sub-block histogram equalization technique can be used. In this method, each sub-block is not overlapped with adjacent sub-blocks and it is histogram equalized by moving the origin of sub-block.

However, this non-overlapped method causes the blocking effect. The reason for the blocking effect can be explained as follows. Considering adjacent sub-blocks in the input image, average brightness of adjacent sub-blocks has similar and the brightness of pixels at the borders of the sub-blocks changes gradually. So, the blocking artifacts don't appear. However, since brightness and size of objects are different within each sub-block, they have different types of histograms. At the result that non-overlapped sub-block histogram equalization is performed, the blocking artifacts are appeared on an adjacent border of the sub-blocks because

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two different sub-block histograms are used as transformation functions.

Methods for reducing the number of iterations for sub-block overlapped histogram equalization have been proposed in reference papers [5],[7]. In other words, the histogram equalization function of an area is obtained by using the weighted sum for histograms of adjacent areas rather than only using the local histogram of relevant area. For example, if 3×3 mask is used, the histogram equalization function in the center of the area can be obtained from the histogram of its own and histograms of adjacent eight regions. Since this mask has the similar form with the low-pass filter for the image and its function is also similar, it blurs an image by reducing the brightness difference between adjacent pixels. Likewise, the form of a histogram equalization function will also be similar in each sub-block. As this result, these papers [5],[7] showed that the blocking artifacts are reduced experimentally. However, as the blocking artifacts can still occur depending on the image characteristics, these papers represented that post-processing such as blocking effect reduction filter must be conducted.

Therefore, since the image size of this paper was determined as FHD resolution to enhance the contrast for large-size LCD TV, the pre-test is needed to derive parameters for the minimum block size which can be applied without causing the blocking artifacts. Through this experiment, we will be able to reduce the computational complexity of sub-block overlapped method and improve the blocking artifacts.

3 Error Diffusion Technique Although original image maintains high quality and good quality can be implemented due to high bit-depth, a case that has to reduce bit-depth of an input image can exist because of the expression ability restriction of display devices. In this case, the original image is quantized, the quantization error will occur.

Fig.2 The block diagram of conventional error diffusion algorithm. In general, when the bit-depth that can display an image to have passed through a quantization process is insufficient, the way to be used to improve effectively image quality is error diffusion (ED) technique. As shown in Fig. 2, the ED algorithm that was proposed for

the first time by Floyd and Steinberg diffuses a quantization error caused by the binarization of pixel values to a surrounding pixel. The quantization error is multiplied by appropriate weighting value of ED mask.

General ED algorithm can be represented by the following equations [8].

),(),(),( _ nmunmXnme outED −= (4)

∑∈

−−−=Rlk

inED lnkmenmwnmXnmu),(

_ ),(),(),(),( (5)

⎭⎬⎫

⎩⎨⎧ ≥

==otherwise

TnmuifnmuQnmX outED ,0

),(,255)),((),(_

(6) Here, e(m,n) is the quantization error, u(m,n) is the state variable. XED_out(m,n) means the pixel values of binarized image and w(m,n) represents the weighting values of ED mask.

Fig. 3 shows the ED mask of Floyd and Steinberg. The weighting values for each quantization error which are located on the right side of Fig.3 are modified in the ED algorithm of Floyd and Steinberg. When implemented in hardware, the product of coefficients can be implemented as simple shift operations without using multipliers. So, hardware designers will be able to significantly reduce the computational complexity of conventional ED by substituting shift operation for multiplication operation.

Fig.3 Floyd-Steinberg’s error diffusion mask.

Fig. 4 shows the propagation flow for ED.

Quantization error is propagated to the right pixel and the three pixels of next horizontal line. Here, looking at the four sides on first frame, the top left side is propagated in the three directions and the top right and bottom right sides are propagated in the two directions and one direction respectively. Conversely, a pixel receives the four error values from neighboring pixels.

Fig.4 Error diffusion propagation flow on first frame.

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In terms of product applications, ED technique has been used to improve the false contour of the PDP (Plasma Display Panel) due to the reduction of gray levels which can be expressed in dark areas [9].

4 The Proposed Contrast Enhancement In order to improve the local brightness and the contouring artifacts together, this paper proposed the image contrast enhancement that combines the optimized histogram equalization and the modified ED methods. The whole architecture of proposed contrast enhancement as shown in Fig. 5 is composed of histogram equalization unit for the local brightness improvement, input parameter definition unit, and modified error diffusion unit.

Fig.5 The whole architecture of proposed contrast enhancement.

First, histogram equalization unit consists of sub-block and step size decision block, histogram equalization through minimal sub-block overlap method, and I2C (Inter Integrated Circuit) communication unit that plays a role to control easily the main parameters through PC users from outside. Here, Xin(m, n) represents the input image with low contrast. In case of color images, it means the luminance signal that is obtained through color space conversion such as RGB to YCbCr or RGB to YCoCg. XHE_out(m, n) represents the result of histogram equalization optimized for large size LCD TV, SShv is sub-block size, Sh and Sv are horizontal and vertical step size respectively.

The decision base of sub-block size can be explained as follows. In LCD TV adopting LED (Light Emitting Diode) backlight, local dimming technique has been used in order to minimize power consumption and light leakage [10]. As shown in Fig. 6, after individual LED devices are arranged on the rear of the LCD TV and are divided into many blocks, the divided blocks are driven by local dimming technique according to the image characteristics. Here, PWM (Pulse Width Modulation) signal of LED devices belonging to each block is determined by using the histogram information of each block. So, when performing histogram equalization, we can use hardware resources effectively and get better contrast enhancement by using sub-block size exactly the same as local dimming block size.

Fig.6 The driving system of LCD TV using LED backlight.

Secondly, conventional ED algorithm was properly

modified to reduce the contouring artifacts occurring after histogram equalization. The modified ED algorithm contains input parameter definition and modified error diffusion units as shown in Figure 5. In order to define the inputs which are used to the modified ED algorithm, average image (Yavg) and scaling of difference image (YDI) is introduced newly.

Modified ED process of Fig. 5 can be expressed as follows.

),('),(),( _ nmYnmYnme avgoutED −= (7)

∑∈

−−−=Rlk

avgavg lnkmenmwnmYnmY),(

),(),(),(),(' (8)

),(),('),( nmYnmYnmu DIavg += (9)

)),((),(_ nmuQnmY outED = (10)

Here, e(m, n) is the difference value that error image is spread by error diffusion, Y'avg(m, n) is the state variable, and u(m, n) represents the image that will be quantized by the multi-level quantizer. Unlike conventional ED algorithm, intermediate variable is set as Y'avg(m, n).

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After adding the average image including the result that the error image is diffused to the scaling of difference image, it is entered into the multi-level quantizer. The average gray level for input and output values isn’t maintained because the modified ED disperses to neighboring pixels properly by multiplying the value of equation (7) and the weighting value of ED mask. Thus, the distortion such as false contour is reduced in flat area due to the reduction of difference values between neighboring pixels.

5 Experimental Results We implement the local histogram equalization (LHE) method having the optimized sub-block size and step size for large-size LCD TV, and then implement the proposed method that combines ED and LHE by using the visual C++ 6.0 environment. Simulations are conducted for FHD images with low contrast and various characteristics where the bit depth is 8-bit. As shown in Fig. 7, sub-block size of LHE (SShv) is 120×90 and it is equal to local dimming block size. Through repeated experiments, overlapped horizontal and vertical step sizes (Sh, Sv) to remove the blocking artifacts were determined as 6 and 9 pixels respectively. We compare the performance from two kinds of perspectives including visual quality and hardware complexity. 5.1 Visual Quality Comparison

Fig.7 The sub-block for local histogram equalization. Fig. 8 shows the visual quality comparison for FHD image with low contrast. Fig. 8(b) is the enlarged image of the flat area in Fig. 8(a), Fig.8(C) is the enlarged result image performing LHE, and Fig. 8(d) represents the result image applying the modified error diffusion algorithm to Fig. 8(c). It is noticed that the contrast is improved through histogram information of the top right section in Fig. 8(c) and the blocking artifacts in the sub-block overlapped LHE isn’t visible. However, the false contour problem occurred due to sharp change of pixel values in the flat area that the brightness value has to change gradually. As shown in Fig. 8(d), since the difference between the input image with low contrast and the image with improved contrast using LHE is dispersed to the neighboring pixels by ED technique, the pixel values are adjusted to change gradually in the flat area. So, we can see that the false contour is reduced.

Fig.8 Visual quality comparison for FHD image with low contrast: (a) original image; (b) original image enlargement for a square area; (c) LHE results; (d) proposed method (the combination LHE and ED).

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5.2 Complexity Comparison The proposed method shows much lower complexity than block overlapped histogram equalization [11] using the overlap of pixel unit because it uses a wide range of step size. Next, when it is compared with POSHE [5] using the weighted sum for histograms of adjacent areas, a computational complexity of histogram equalization is the same. But, as POSHE uses the blocking effect reduction filter to prevent the blocking artifacts additionally, 3 line memory is needed. On the other hand, as the proposed method uses the modified ED technique to improve the false contour, 2 line memory is needed. Therefore, the proposed method can maximize the contrast image quality by having a minimal computational complexity.

6 Conclusion We have developed the image contrast enhancement algorithm that combines the optimized histogram equalization and the modified ED methods in order to improve the local brightness and the contouring artifacts together. We derived the sub-block size and step size of local histogram equalization that can be applied without causing the blocking artifacts for large-size LCD TV. This experimental results show that the proposed method preserves the local image brightness and suppresses the contouring artifacts, compared to the method using only local histogram equalization. The proposed module can be incorporated into the image processor that is used to control the images of LCD TV or the timing controller that provides the control signals for flat panel displays.

Acknowledgment This work has been supported by the MKE (The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2010-C1090-1001-0006). References: [1] R. C. Conzalez and R. E Woods, Digital Image

Processing, New Jersey: Prentice-Hall, Inc., 2001. [2] S. D. Chen and A. Rahman Ramli, “Contrast

enhancement using recursive mean-separate histogram equalization for scalable brightness preservation,” IEEE Trans. on Consumer Electronics, Vol.49, No.4, pp.1301-1309, 2003.

[3] Z. Chen and B. R. Abidi, “Gray-level grouping (GLG) : An automatic method for optimized image contrast enhancement-part I : The basic method,” IEEE Trans. on Image Processing, Vol.15, No.8, pp.2290-2302, 2006.

[4] S. M. Pizer et al, “Adaptive histogram equalization and its variations,” Computer Vision Graphics and

Image Processing, Vol.39, pp.355–368, 1987. [5] J. Y. Kim, L. S. Kim, and S. H. Hwang, “An

advanced contrast enhancement using partially overlapped sub-block histogram equalization,” IEEE Trans. on Circuits and Systems for Video Technology, Vol.11, No.4, pp.475-484, 2001.

[6] Yeong-Taeg Kim, “Contrast enhancement using brightness preserving bi-histogram equalization,” IEEE Trans. on Consumer Electronics, Vol.43, No.1, pp.1-8, 1997.

[7] K. S. Sim, C. P. Tso, and Y. Y. Tan, “Recursive sub-image histogram equalization applied to gray scale images,” Pattern Recognition Letters, Vol.28, No.10, pp.1209-1221, 2007.

[8] T. Liu, “Probabilistic error diffusion for image enhancement,” IEEE Trans. on Consumer Electronics, Vol. 53, No. 2, pp. 528-534, May 2007.

[9] S.-J. Kang, H.-C. Do, and J.-H. Shin, “Reduction of low gray-level contours using error diffusion based on emission characteristics of PDP,” IEEE Trans. on Consumer Electronics, vol.50, No.2, May. 2004.

[10] H. Chen, J. Sung, T. Ha, and Y. Park, “Locally pixel-compensated backlight dimming on LED-backlit LCD TV” Journal of the SID, 15/12, pp. 981-988, 2007.

[11] T. K. Kim, J. K. Paik, and B. S. Kang, “Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering,” IEEE Trans. on Consumer Electronics, Vol. 44, No. 1, pp. 82-86, Feb. 1998.

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