Improved block based segmentation for jpeg

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 669
IMPROVED BLOCK BASED SEGMENTATION FOR JPEG
COMPRESSED DOCUMENT IMAGES
P.Mahesh1
, P.Rajesh2
, I.Suneetha3
1
M.Tech Student, 2
Asst Professor, 3
Assoc Professor, Annamacharya Institute of Technology and Sciences, AP, India,
p.maheshreddy169@gmail.com, rajshjoyful.smily@gmail.com, iralasuneetha.aits@gmail.com
Abstract
Image Compression is to minimize the size in bytes of a graphics file without degrading the quality of the image to an unacceptable
level. The compound image compression normally based on three classification methods that is object based, layer based and block
based. This paper presents a block-based segmentation. for visually lossless compression of scanned documents that contain not only
photographic images but also text and graphic images. In low bit rate applications they suffer with undesirable compression artifacts,
especially for document images. Existing methods can reduce these artifacts by using post processing methods without changing the
encoding process. Some of these post processing methods requires classification of the encoded blocks into different categories.
Keywords- AC energy, Discrete Cosine Transform (DCT), JPEG, K-means clustering, Threshold value
--------------------------------------------------------------------***----------------------------------------------------------------------
1. INTRODUCTION
JPEG is one of the most widely used block based image
compression technique. Although JPEG was first developed
for natural images, in practice, it is also used for document
images due to its simple structure. However, document
images encoded by the JPEG algorithm exhibit undesirable
compression artifacts. In particular, ringing artifacts
significantly reduce the sharpness and clarity of the text and
graphics in the decoded image. Many decoding schemes have
been proposed to improve the quality of JPEG encoded
images. Such methods neither require modification to current
encoders, nor an increase in bit rate. Compression of a
compound image is more critical than natural image.
Basically a compound image is a combination of text,
graphics and pictures. Most methods use different algorithms
for various functionalities. Compound image compressions
normally based on the following categories: Object Based,
Layer Based and Block Based
Most of the recent researches in this field mainly based on
either layer based or block based. In object Based method a
page is divided into regions, where each region follows exact
object boundaries. An object maybe a photograph, a graphical
object, a letter, etc. The main drawback of this method is its
complexity. In layer-based method, a page is divided into
rectangular layers. Most of the Layer based methods follow
the 3-layer MRC model. The basic three layers MRC model
represents a color image as two color-image layers
[Foreground (FG), Background (BG), and a binary Mask
layer (mask)]. The mask layer describes how to reconstruct
the final image from the FG/BG layers. All these methods
first divides the JPEG encoded image into different classes of
regions and each region is decoded by using algorithm
specially designed for that class of region. This region
selection is done on the block basis, i.e. each encoded block
is first classified as one of the required region. This paper
focuses on jpeg coded document images. In a discriminative
function based on DCT coefficients is calculated for every
block. This function value is compared with a threshold
parameter to classify the block. Discriminative function is
defined as follows:
∑∑= =
=
7
0
7
0
)],([)(
u
s
v
f vuygsD (1)
Where =)(xgi







≠
otherwise
andxifx
0
0|||)(|log2
ys(u,v) is DCT coefficients of blocks ‘s’
Df(s) ≈0 => ‘s’ is a background block
if Df(s) ˂T => ‘s’ is a picture block
if Df(s) ≥ T => ‘s’ is a Non picture block
Threshold value proposed in above segmentation algorithm is
not robust to different quantization tables used in baseline
JPEG. Threshold value proposed gives good results, when
default quantization matrices mentioned by JPEG standard
are used. But when user defined Quality factor (QF) changes,
obviously quantization matrices will change. It leads to

__________________________________________________________________________________________
misclassification of the blocks. In for detection of background
blocks AC energy thresholding is used. Next two feature
vectors are developed for remaining each block. Authors
characterized the feature vectors of the text blocks and those
of the picture blocks by two Gaussian mixture models.
Authors used these two Gaussian mixture models with the
SMAP segmentation algorithm to segment the feature vector
image. The result is combined with the background blocks
detected by AC thresholding to produce the final
segmentation map. This segmentation algorithm is robust to
different quantization tables used in JPEG, but computational
complexity is high.
Fig. 1: Block based segmentation
In this paper propose a block based segmentation algorithm
for document images encoded by conventional JPEG
encoder. Our algorithm works by using AC energy
thresholding for background blocks segmentation and for
remaining blocks classification K-means clustering of feature
components generated for each block.
2. PROPOSED MODEL
The first model uses the AC coefficients introduced during
DCT to segment the image into three blocks, background,
text /graphic and image blocks. The background block has
smooth regions of the image. While the text / graphics block
has high density of sharp edges regions and image block has
the non-smooth part of the compound image.
Fig 2: AC Coefficient Based Model
AC energy is calculated from AC coefficients and is
combined with a user-defined threshold to identify the
background block initially. It will consider 8×8 luminance
blocks as input and classifies them as background or picture
or text block. AC energy (Es) of each block‘s’ is calculated
as:
∑=
=
63
1
2~
,
i
iss yE (2)
Where,
~
,isy is the ith
quantized DCT coefficient of the 8×8
block s. If Es is smaller than the threshold value, then that
block is classified as a background block. Next, we compute
two-dimensional feature vectors for each block in order to
classify the remaining blocks into the text and picture classes.
The first feature component is based on the encoding length
as proposed in . The encoding length of a block is defined as
the number of bits in the JPEG stream used to encode that
block. Typically, text blocks consists of high contrast edges
as compared to other blocks, so encoding lengths for text
blocks are longer than for the non text blocks. However, the
encoding length also depends on quantization step sizes used.
Larger the quantization steps, smaller the encoding length and
vice versa. As from codec of Baseline-JPEG quantization step
sizes are depends up on the user defined Quality Factor (QF).
Quantization step sizes are inversely proportional to QF. So
smaller the QF, smaller the encoding length and vice versa.
To make the segmentation more robust to different
quantization step sizes, we multiply the encoding length by a
factor (λ), determined from the quantization steps used in
encoding of JPEG. Suppose Qi* are the default luminance
quantization step sizes as defined in Table K.1 in, and Qi are
the quantization step sizes used to encode the luminance
component. Quantity λ is used as a measure of the coarseness
of the quantization step sizes Qi as compared to the default
PDF Image
Divide into 16*16 Blocks
DCT-AC Coefficients
Non Smooth Blocks
K-means Clustering
Text Image
Smooth block
K-means Clustering

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Qi*. Larger quantization step sizes (Qi) corresponds to larger
values of λ and vice versa.
∑
∑
=
i
ii
i
ii
QQ
QQ
)(
)(
**
*
λ (3)
First feature component of the blocks is defined as:
×= γ
λ1,sD (Encoding length of blocks) (4)
Threshold value used for AC thresholding and γ used in Ds, 1
are referred from . This value are determined from training
process and then uses a set of training images consisting of
fifty four digital and scanned images. Each image is manually
segmented and JPEG encoded with nine different
quantization matrices, corresponding to λj with j=1 to 9. For
the i th
image encoded by the j
th
quantization matrix, they first
computed the average encoding lengths of the text blocks and
the picture blocks, denoted by ui,j and vi,j respectively. The
parameter γ is then determined from the following
optimization problem:
])()[(minminargˆ
54
1
9
1
2
,
2
,
,
∑∑= =
−+−=
i j
jijij
vu
vvuu γγ
γ
λλγ (5)
where u, v are average encoding lengths of the text blocks
and the picture blocks by using default quantization step size
as defined in Table K.1 in. It is determined as γ=0.5 and
threshold value as 200 (ac ε =200).
Due to multiplication of λ with the encoding length, effect of
quantization step sizes on Ds,1 will be reduced. Because,
larger quantization step sizes leads to small values of
encoding length and large values of λ, but the product Ds,1
remains same. Similarly, for smaller quantization step sizes
the same will occur. So the range of Ds,1 for a particular class
of block does not change with the quantization step sizes or
QF. It depends only on the class of blocks and value is high
for text class blocks as compared to remaining blocks.
Generally text block is a combination of two colors
foreground intensity and background intensity. So to take this
advantage second feature component (Ds,2) is developed to
find how close a block is to being a two color block. We take
the luminance component decoded by the convectional JPEG
decoder and use k-means clustering [6] to separate the pixels
in a 16×16 window centered at the block s into two groups.
Let C1, s and C2, s denote the two cluster means. Then second
feature component is computed by the difference of two
cluster means, given in Eq.(6)
|| ,2,12, sss CCD −= (6)
If difference of two cluster means is high, value of Ds,2 is
large, means block is a two color block. Otherwise block is a
single color block. As explained above, text block is
combination of two colors, so obviously Ds,2 value is high for
text class blocks as compared to remaining blocks. Compute
vector Ds, for every non background block is defined sum of
two feature components as:
2,1, sss DDD += (7)
As explained above for text blocks Ds,1 and Ds,2 are high
when compared to picture blocks. So their sum Ds is also
high value for text blocks compared to picture blocks. So we
divide this vector Ds into two clusters by using k-means
clustering and the resulting clusters with high mean value are
segmented as text blocks and remaining blocks are named as
picture blocks.
Fig. 3: K-means Clustering Analysis
K-means clustering is a method of cluster analysis, which
partitions the given n observations into required k clusters
such that each observation belongs to the cluster with the
nearest mean. So for a given a set of observations (x1, x2….
xn), k-means clustering aims to minimize the sum of squares
within each cluster S={S1, S2 … Sk},:
∑∑=
−
k
i sx
ij
s
ij
x
1
2
||||minarg
ε
µ (8)
Where iµ is the mean of all points in Si.

__________________________________________________________________________________________
3. SIMULATION RESULTS
Proposed algorithm performance can be evaluated by taking
seven different images. Each of these images contains some
text and/or graphics. Segmentation algorithm results for all
test images encoded by baseline JPEG at different
compression ratios. Consider a test image-1 as in Fig.
1.Segmentation results of JPEG encoded test image-1 at QF
values of 10, 25, and 60 are shown below in Fig. 4, Fig. 5 and
Fig. 6 respectively. In resultant images, red color blocks
represent the text region, yellow color blocks represent
picture region and white color blocks represent background
blocks. For all QF values, proposed segmentation gives
almost same classification of regions.
Fig. 4: Original Test Image-1
Fig. 5: Segmentation Results (QF=10)
CONCLUSIONS
Block based segmentation algorithm for JPEG encoded
document images has been successfully implemented by
using MATLAB software. Visual inspection of the simulation
results indicate that proposed algorithm is robust to different
quantization step sizes used in JPEG encoding. This paper
considers true color images only. Block segmentation
techniques can also be extended to gray scale images by
small changes in the algorithm and take it as other image
formats. The future work is the development of segmentation
algorithm for documented images input take it as other image
formats exclude JPEG.
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BIOGRAPHIES
Mr. P. Mahesh received the B.Tech
Degree in E.I.C.E from N.B.K.R.IST,
Vidyanagar, Nellore under Affliated Sri
Venkateswara University (SVU), He is
pursuing M.tech Degree with the
Specialization of ECE (DSCE) in
Annamacharya Institute of Technology and Sciences (AITS),
Tirupati, AP, India
MS. I. Suneetha received the B.Tech and
M.Tech Degrees in E.C.E from Sri
Venkateswara University College of
Engineering (SVUCE), Tirupati, India in
2000 and 2003 respectively.
She is pursuing her Ph.D Degree at
SVUCE, Tirupati and working as
Associate Professor & Head, E.C.E department,
Annamacharya Institute of Technology and Sciences (AITS),
Tirupati. Her teaching and research area of interest includes
1D & 2D signal processing.

Improved block based segmentation for jpeg

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