Nur Azman Abu et al. Journal of Computer Science 9 7: 811-820, 2013
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where, i = {H,V,D}, j is the starting scale, the
w
j
j ,m,n coefficients define the approximation of
fx,y,
i Ψ
W j, m, n
coefficients represent the horizontal, vertical and diagonal details for scales j≥j
. Here j = 0
and select N+M = 2
j
so that j = 0,1 and m, n = 0,1. Then the inverse of the DWT is given in 9 as follows
Equation 9 Ye et al., 2009:
j m
n ¥
i i
Ψ m
n j ,m,n
j ,m,n i= H,V D = j0
j
1 fx, y =
W j , m, nj x, y +
MN 1
W j , m, nΨ x, y
MN
∑ ∑ ∑ ∑ ∑∑
9
2.2. Tchebichef Moment Transform
Tchebichef moment transform is a transform method based on discrete orthogonal Tchebichef polynomials
Mukundan et al., 2011, which carry energy compactness properties for both graphical and natural
images. For a given set {t
n
x} of input a value image intensity values of size N = 2, the forward discrete
orthogonal Tchebichef Moments of order m + n is given as follows Equation 10 Ernawan et al., 2012:
1 1
m n
mn x =0 y =0
1 T
= t xt yfx, y
ρ m, Mρn, N
∑ ∑
10 where m = 0, 1 and n = 0, 1. f x, y denotes the intensity
value at the pixel position x, y in the image. The t
n
x are defined using the following recursive relation
Equation 11 and 12:
t x = 1
11
1
2x + 1 - N t x =
N
12 The set {t
n
x} has a squared-norm given by Equation 13:
{ }
1 2
i i=0
ρ
n, N = t x
∑
2 2
2 2
1 2
N × 1 - × 1 -
N N
= 2n + 1
13 The process of image reconstruction from its
moments, the inverse moment Tchebichef moments are given in 14 as follows Equation 14:
1 1
m n
mn m=0 n = 0
= T t xt y
fx, y
∑ ∑
ɶ 14
for m = 0, 1 and n = 0, 1. where,
fx, y ɶ
denotes the reconstructed intensity distribution. Tchebichef moment transform has its
own advantage in image processing which has not been fully explored. The TMT involves only algebraic
expressions and it can be computed easily using a set recurrence relation 10-13.
3. AN EXPERIMENTAL DESIGN
In this experiment, Floyd Steinberg method, DWT and TMT shall be used on image dithering. This
experimental used 80 sample images containing 40 natural images and 40 graphical images. The samples of
40 natural images and 40 graphical images of size 512
× 512 pixels were analyzed and evaluated on image
dithering. A sample true colour of natural and graphical images with size 512
×
512 pixels is shown in Fig. 2 and 3.
Originally images are 24-bit RGB image with the size of 512
× 512 pixels. They are reduced to 4-bit using the
popular clustering technique, K-means method.
3.1. K-Means Algorithm
The mapping or initial colour palette is accomplished by clustering the colour partition into a number of
distinct groups. This study used K-Mean clustering to distinguish colour for displaying the image with a limited
colour palette. The initial palette colour is chosen based on K-means algorithm. This approach is obtained to
reduce the bit depth from 8-bit into 4-bit colour image.
The clustering approaches can be categorized into partition and hierarchical clustering algorithms. The
partition clustering algorithms such as K-means clustering is a popular technique for clustering colour
image processing by partitions data set into k sets or a number of distinct groups. When an image is quantized
into a limited number of colours, the colour regions are clustered into one of the colours in the palette. The
results of the degradation among the colours can still be perceived by human eye.
Typically K-mean begins with k arbitrary centers chosen uniformly at random from the data points. Each
point is then assigned to the nearest center and each center is recomputed as the center of mass of all points assigned
to it. These two steps assignment and center calculation are repeated until the process stabilizes. K-Means
algorithm attempts to find the cluster centre s
1
, ..., s
j
, that sum of squared distance of each data point x
i
to its nearest cluster centre s
j
is minimized. The membership for each data point belongs to nearest centre depends on minimum
distance. This membership is given in 15 as follows Equation 15 Shah and Singh, 2012:
Nur Azman Abu et al. Journal of Computer Science 9 7: 811-820, 2013
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Science Publications
JCS
Fig. 2.
Sample of 24-bit RGB of natural image with 512
×
512 pixels
Fig. 3.
Sample of 24-bit RGB of graphical image with 512
×
512 pixels
N 2
i j
i=1 j 1,...,k
Mx,s = min
x - s
∈
∑
15 where, N refers to numbers of data point, x
i
is each data point, s
j
is cluster centre and k is the number of clusters the data will be partitioned. The outputs of natural and
graphical images with 4-bit of size 512×512 pixels using K-Means algorithm are shown in Fig. 4 and 5.
Fig. 4.
The sample 4-bit RGB of natural image left with 512
×
512 pixels
Fig. 5.
4-bit RGB of graphical image with 512
×
512 pixels
The experimental results of reduced colour using K-
Mean as presented in Fig. 4 and 5 shows that the image outputs has much lower quality than original 24-bit RGB
image. Another adverse effect of reducing the bit-depth of colour image gives a smearing effect surrounding an edge.
The colours on two sides of the edge are smeared to each
Nur Azman Abu et al. Journal of Computer Science 9 7: 811-820, 2013
815
Science Publications
JCS
other and sharp edges are converted to jagged edges. In this experiment, the colour image has been clustered into
16 clusters. The number of clusters is determined to classify number of colour images.
Next, the 4-bit RGB image with 16 colours is divided into 2×2 pixel blocks of pixels. Each block is transformed
from the pixel domain to the moment coefficient by 2×2 TMT. The weight filter table is used to adjust the image
output. Next, the error diffusion for TMT is proposed to distribute errors among its pixels. 2×2 TMT is implemented
to achieve better performance on image dithering. During the image dithering, 2×2 blocks of pixels are
processed from left to right and from top to bottom. In this experiment, 2×2 sub block image becomes useful
due to its size advantage in the image dithering. A 2×2 sub block image becomes popular and efficient in image
reconstruction Ernawan et al., 2012. Next, each block of the image is computed with 2×2 orthogonal
Tchebichef polynomials independently. Having large number of pixels on true colour images,
K-Means may be computationally faster than hierarchical clustering. This palette is representing the pixel colours in
the sample above. The palette tables specification of the 4- bit colours on natural image and graphical image of size
512×512 pixels are presented in Table 1 and 2 respectively. The visualization of TMT colour image dithering scheme
is presented in Fig. 6.
3.2. Moment Coefficients
