International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 6 - Mar 2014
A New Image Enhancement Algorithm Using Just
Noticeable Difference and Histogram Equalization
A.Anitha(PG scholar)
Prof.B.Rajesh Kumar,M.E(Ph.d)
RVS College of Engineering and Technology
RVS College of Engineering and Technology
Coimbatore
Coimbatore
Tamilnadu
Tamilnadu
1 INTRODUCTION
ABSTRACT
One common way to save the battery life of a
portable device is to reduce the LCD backlight
intensity, but result in poor quality image. The
dim backlight image enhanced to bright image
especially for the low-luminance image areas. It
has been envisioned as the de-facto solution to
the quality image costs of electronic devices such
as smart phones. The enhancement algorithm
proposed in this paper brightness and visibility
prediction for dim image. Based on just noticeable
difference (JND) theory and the Human Visual
System response model, the algorithm effectively
enhances the visibility of image details in dark
regions without affecting the contrast of bright
regions. Apply the just noticeable difference
theory and find the histogram bin finding. This
shows the bin table. Finally apply the histogram
equalization method. The boosting and color
restoration are performed. The purpose of using
visibility prediction and brightness for dim
backlight image without detail loss and color
degradation.
Keywords- LCD backlight JND theory,
Bin finding, Histogram equalization and image
enhancement.
ISSN: 2231-5381
The LCD backlight of a portable multimedia device
to a low power level saves energy but results in poor
image quality especially for the Low-luminance
image areas. When the LCD backlight intensity is
sufficiently low, it can hardly see that the detail of
the dark regions becomes less visible and the image
color. This depends on the luminance of the ambient
light. The brighter the ambient light, the higher the
threshold value. This HVS property serves as the
principle for our algorithm, which aims at restoring
the detail of dark image regions. When illuminated
with dim backlight without affecting the appearance
of the other regions. To save the energy for dim
backlight image. The enhancement algorithm can
provide quality image without detail loss. Based on,
Just Noticeable Difference theory,(JND) and the
Histogram equalization.This enhances the visibility
of the low-luminance area.
1.1CHARACTERISTICS OF LCD
The LCD architecture is required to the backlight
scaling problem. Fig.1 (a) shows a typical
architecture of the LCD controller and panel. The
LCD controller receives the video data and
determines a proper grayscale-i.e., the each pixel
based on its Pixel value.
http://www.ijettjournal.org
Page 282
International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 6 - Mar 2014
Depending on the light source, there are three kinds
of LCD displays. One is reflective LCD, which uses
ambient light and a reflector instead of the backlight
To preserve the perceptual contrast of the bright
region, its luminance is reallocated to a luminance
range slightly smaller than the perceptible luminance
range. In the meanwhile, to enhance the perceptual
contrast of the dark region, the luminance of the dark
region is compressed to a small range and boosted
above the minimum perceptible level. This way, the
enhancement of the dark region is achieved at only as
light cost of the luminance range of the bright region.
Hence, the effect on the perceptual contrast of the
bright region is very small.
2 METHODS
2.1 EXISTING SYSTEM
Fig.no1
(a) LCD System Architecture
but is unsuitable for quality display. Another is LCD,
which requires the backlight and the ambient light to
operate in a complementary way. The third one,
which is what this paper is concerned about, is LCD
that illuminates all pixels from behind.
Unlike the other components in portable devices, the
light source of LCDs cannot be shut down to prolong
battery life. When the backlight is turned off, LCD
displays nothing and LCD delivers poor display
quality.
Most previous methods for enhancing dimmed
images deal with 50% or more LCD backlight. The
existing systems before using methods are ABIE.
When the LCD backlight intensity is sufficiently low,
it can hardly see the content of an image. The detail
of the image becomes invisible when the luminance
is below a certain threshold, which depends on the
luminance of the ambient light. The brighter the
ambient light, the higher the threshold is. This HVS
property serves as the guiding principle for our
algorithm, which aims at restoring the detail of dark
image regions when illuminated with dim backlight
without affecting the appearance of the other regions.
2.1.1 ABIE AND TABS METHODS
1.2 DRAWBACKS OF DIM BACKLIGHT ON IMAGES
Dim backlight affects the visual perception of an
LCD image in two ways.
1) First, it causes the image details;
especially those of the dark regions, less visible or
even imperceptible. This is referred to as the “detail
loss effect” in this paper.
2) Second, it causes color degradation
because of the decrease of chrominance intensity.
The dimmer the backlight is, the more the color
degrades.
ISSN: 2231-5381
The ABIE method applies brightness compensation
to the low frequency part of an image and local
contrast enhancement to the high frequency part of
the image. Two approaches to image enhancement
for backlight-scaled LCD have been developed.
1).One minimizes the power consumption of
LCD backlight subject to a constraint on image
quality.
2).The other optimizes the image quality for
given LCD backlight level.
http://www.ijettjournal.org
Page 283
a
International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 6 - Mar 2014
Scaling is applied to the pixels of an image uniformly
without taking the local contrast property of the HVS
into consideration. As a result, pixels of the same
intensity, regardless of location, remain equal in
intensity after the transformation. This is inconsistent
with the local contrast property of the HVS and may
introduce image distortion.
2.1.2. DRAWBACKS
2.2.1 JUST NOTICEABLE DIFFERENCE
The Difference Threshold (or "Just Noticeable
Difference") is the minimum amount by which
stimulus intensity must be changed in order to
produce a noticeable variation in sensory experience.

It is denoted by ∆L
∆L = 0.0594(1.219+L0.4)2.5
The main drawbacks of existing system are:
1) These methods suffer from detail loss and
color degradation for the scenario
considered in this work.
2) Over-enhancement of dark regions is
another common drawback of these
methods.
2.2 PROPOSED SYSTEM
The proposed methods for enhancing dimmed images
deal with 10% LCD
backlight. These methods
solving the detail loss and color degradation. The
algorithm effectively enhances the visibility of image
details in dark regions without affecting the
perceptual contrast of bright regions. The algorithm
also applies appropriate counter shading to eliminate
halo effect and, enhance perceptual contrast of the
backlight-scaled image.
Where J (L) is a function that returns the JND of the
given luminance.
2.2.2 HISTOGRAM
In DSP, we store the number of pixels (frequencies)
of same intensity values into a histogram array,
which is commonly called "bin". For an 8-bit
grayscale image, the size of histogram bin is 256,
because the range of the intensity of 8-bit image is
from 0 to 255.
Histogram of an 8-bit grayscale image
Histogram is a useful tool to analyze the brightness
and contrast of an image. It shows how the intensity
values of an image is distributed and the range of
brightness from dark to bright. An image can be
enhanced by remapping the intensity values using the
histogram. Also, histogram is used to segmentize an
image into the several regions by thresholding. For
example, if the image intensities in the histogram are
divided into 2 groups, the threshold value can be
chosen at the middle of 2 peaks in the
histogram.Building a histogram of an image is quite
easy. Traverse all pixels and count up the intensity
values.
3 MODULES OF IMAGE ENHANCEMENT
3.1LUMINANCE EXTRACTION
Fig. no (2) input image with 10% backlight
ISSN: 2231-5381
The Luminance is thus an indicator of how bright the
surface will appear Luminance. Here extract the
luminance from Input image and find the luminance
value. After backlight intensity enhances the extract
layer.
http://www.ijettjournal.org
Page 284
International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 6 - Mar 2014
3.2 JND THEORY
An image is decomposed, and need to construct an
HVS response function first. Response function used
in this work is developed based on the human visual
model. And it models the response of HVS to a
foreground luminance LF. Given a background
luminance LB. To apply the HVS response function
to find the response of each pixel, and define the
background and foreground luminance.
The JND decomposition methods can divide into two
layers:
i) HVS response layer.
In histogram equalization, the input pixel intensity, x
is transformed to new intensity value, x′ by T. The
transform function, T is the product of a cumulative
histogram and a scale factor. The scale factor is
needed to fit the new intensity value within the range
of the intensity values.
= ( ) =
Where
.
.
is the number of pixels at intensity ,
is the total number of pixels in an image.
ii)Background luminance layer.
The JND decomposition can apply the low pass filter
and this model can produce the above two layers.
After HVS response layer combine into the JND
composition and the background layer can combine
to the threshold value. The decomposition can
perform the two layers are base layer and detail layer.
3.4 HISTOGRAM EQUALIZATION
Histogram equalization redistributes the pixel
intensity values evenly by using cumulative (sum)
histogram as a transfer function or as a look-up table.
3.5COLOR RESTORATION
And perform inverse JND decomposition, which
takes the HVS response value and the boosted and
compressed background luminance value of each
pixel as input and generates the enhanced luminance
value as output. Denote the enhanced luminance
layer image by Le.
Then, the enhanced color image is obtain by,
=
The idea of histogram equalization comes from the
cumulative histogram. The frequencies (the number
of pixels) at each intensity value are accumulated.
The higher pixel density is, the greater steepness is.
There are 2 intervals with same width, but different
slopes; one interval has slow steepness, and the other
interval has steep slope. Let's project the intervals of
the intensity values onto the other axis. Once they are
projected, they have different intervals on the
projected axis. If the slope is less than 1, the interval
gets narrower. If the slope is greater than 1, then the
interval is going to be wider. In other words, we are
going to spread the intensity range if the pixel density
is high, and shrink the interval if the density is low.
Histogram equalization works best on an over or
under exposed image, which has narrow contrast
range.
ISSN: 2231-5381
( ) Where Lo is the luminance value of the original
image is the Gamma parameter of the display, and
Mo and Me, respectively, are the original and
enhanced pixel values of a color channel. This
operation is performed on every pixel in the image.
http://www.ijettjournal.org
Page 285
International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 6 - Mar 2014
content. The algorithm also applies appropriate
counter shading to eliminate halo effect and,
meanwhile, enhance perceptual contrast of the
backlight-scaled image.
5.1 FUTURE ENHANCEMENT
As future work, an advanced backlight dimming
technique for Histogram equalization, that preserves
the quality of color, and details in images. And one
common way to extend the battery life for user
application such as smart phones and camera. And
another method for JND it is very easy to enhance the
visibility of the dim image. Main advantage of color
shift problem and various type of display technology.
REFERENCES
Fig. no (3.4) output scaled image
4 RESULTS & DISCUSSION
The inconsistency between the subjective and the
objective evaluation results and find that image
details of the dark regions are more likely to become
invisible for ABIE when the images are displayed
with dim backlight. The invisibility of image details
causes ABIE to receive the lowest subjective
evaluation score. The ABIE method applies
brightness compensation to the low frequency part of
an image and local contrast enhancement to the high
frequency part of the image.
However, this weakness is not reflected in the
objective evaluation score because the difference in
the visibility of dark regions between the original and
the enhanced images is not considered by the image
quality metrics adopted for the objective evaluation.
The inconsistency by incorporating the visibility of
dark pixels into the objective quality metric has been
proposed in this paper as a more appropriate input
image.
5 CONCLUSIONS
In this paper, enhancement algorithm to enhance
backlight-scaled images for extremely dim backlight
scenarios where the LCD backlight may drop to
10%.Based on JND theory and the HVS response
model, the algorithm effectively enhances the
visibility of image details in dark regions without
affecting the contrast of bright regions and detail of
ISSN: 2231-5381
[1].Tai-Hsiang Huang, Kuang - Tsu Shih, Su-Ling
Yeh, and Homer H. Chen, “Enhancement of
Backlight-Scaled Images” IEEE Trans.2013.
[2].P.-S. Tsai, C.-K. Liang, T.-H. Huang, and H. H.
Chen, “Image enhancement for backlight-scaled
TFT-LCD displays,” IEEE Trans. Apr. 2009
[3].A. Iranli, W. Lee, and M. Pedram, “HVS-aware
dynamic backlight scaling in TFT-LCDs,” IEEE
Trans. Very Large Scale Integration Syst.,vol. 14, pp.
1103–1116, Oct. 2006.
[4].T. H. Kim, K. S. Choi, and S. J. Ko, “Backlight
power reduction using efficient image compensation
for mobile devices,” IEEE Trans. Consumer
Electron., vol. 56, no. 3, pp. 1972–1978, Aug. 2010.
[5]. Y. Rao and L. Chen, “A survey of video
enhancement techniques,” J. Inf. Multimedia Signal
Processing, vol. 3, no. 1, pp. 71-99, Jan. 2012.
[6] E. P. Ong, X. Yang, W. Lin, Z. Lu, S. Yao, X.
Lin, S. Rahardja, and B.C. Seng, “Perceptual quality
and objective quality measurements of compressed
videos,” J. Visual Commun. Image Representation,
vol. 17,no. 4, pp. 717–737, Aug. 2006.
[7].W. C. Cheng and M. Pedram, “Power
minimization in a backlit TFT-LCD display by
concurrent brightness and contrast scaling,” IEEE
Trans. Consumer Electron., vol. 50, no. 1, pp. 25–32,
Feb.2004.
http://www.ijettjournal.org
Page 286
International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 6 - Mar 2014
[8].S. I. Cho, S. J. Kang, Y. H. Kim, “Image qualityaware backlight dimming with color and detail
enhancement techniques,” J. Display Tech., vol. 9,
no.2,pp. 112–121, Feb.2013.
[9]. G. Krawczyk, K. Myszkowski, H.P.Seidel
“Contrast restoration by adaptive countershading,” in
Proc.Eurographics vol. 26 of Computer Graph.
Forum. Ames, IA: Wiley-Blackwell, 2007, pp. 51-60.
[10]. T.-H. Huang, C.-T. Kao, Y.-C. Chen, S.-L. Yeh,
and H. H. Chen, “A visibility model for quality
assessment of dimmed images,” in Proc. 4th IEEE Int.
Workshop Multimedia Signal Process., pp. 206–211,
Banff, Canada, Sept. 2012.
[11]. T. O. Aydin, R. Mantiuk, K. Myszkowski, and
H. P. Seidel, “Dynamic range independent image
quality assessment,” ACM Trans. Graph., vol.
27, no. 3, article no. 69, pp. 1–10, Aug. 2008.
[12]. Y. Li, L. Sharan, and E. H. Adelson,
“Compressing and companding high dynamic range
images with subband architecture,” ACM Trans.
Graph., vol. 24, no. 3, pp. 836–844, Jul. 2005.
[13]. R. Mantiuk, K. Myszkowski, and H. P. Seidel,
“A perceptual framework for contrast processing of
high dynamic range images,” ACM Trans.Appl.
Perception, vol. 3, no. 3, pp. 286–308, Jul. 2006.
[14]. Z. Farbman, R. Fattal, D. Lischinski, and R.
Szeliski, “Edge-preserving decompositions for multiscale tone and detail manipulation,” ACM
Trans. Graph., vol. 27, no. 3, Aug. 2008.
[15]. F. Durand, and J. Dorsey, “Fast bilateral
filtering for the display of high-dynamic-range
images,” ACM Trans. Graph., vol. 21, no. 3, pp.
257–266, Jul. 2002.
ISSN: 2231-5381
http://www.ijettjournal.org
Page 287