Hybrid JPEG Compression Using Histogram Based Segmentation

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 8, November 2010

Hybrid JPEG Compression Using Histogram Based
Segmentation
M.Mohamed Sathik1 , K.Senthamarai Kannan2 and Y.Jacob Vetha Raj3
1
Department of Computer Science, Sadakathullah Appa College, Tirunelveli, India
mmdsadiq@gmail.com
2,3
Department of Statistics, Manonmanium Sundaranar University, Tirunelveli, India
senkannan2002@gmail.com
jacobvetharaj@gmail.com

Abstract-- Image compression is an inevitable solution for lossy compression algorithms obtain high compression
image transmission since the channel bandwidth is limited and ratios by exploiting human visual properties.
the demand is for faster transmission. Storage limitation is also Vector quantization [4],[5] , wavelet transformation
forcing to go for image compression as the color resolution and [1] , [5]-[10] techniques are widely used in addition to
spatial resolutions are increasing according to quality various other methods[11]-[17] in image compression. The
requirements. JPEG compression is a widely used compression
problem in lossless compression is that, the compression
technique. JPEG compression method uses linear quantization
and threshold values to maintain certain quality in an entire ratio is very less; where as in the lossy compression the
image. The proposed method estimates the vitality of the block compression ratio is very high but may loose vital
of the image and adapts variable quantization and threshold information of the image. Some of the works carried out in
values. This ensures that the vital area of the image is highly hybrid image compression [18]-[19] incorporated different
preserved than the other areas of the image. This hybrid compression schemes like PVQ and DCTVQ in a single
approach increases the compression ratio and produces a image compression. But the proposed method uses lossy
desired high quality output image. compression method with different quality levels based on
. the context to compress a single image by avoiding the
difficulties of using side information for image
Key words-- Image Compression, Edge-Detection, decompression in [20].
Segmentation. Image Transformation, JPEG, The proposed method performs a hybrid
Quantization. compression, which makes a balance on compression ratio
and image quality by compressing the vital parts of the
I. INTRODUCTION image with high quality. In this approach the main subject
Every day, an enormous amount of in the image is very important than the background image.
information is stored, processed, and transmitted digitally. Considering the importance of image components, and the
Companies provide business associates, investors, and effect of smoothness in image compression, this method
potential customers with financial data, annual reports, segments the image as main subject and background, then
inventory, and product information over the Internet. And the background of the image is subjected to low quality
much of the online information is graphical or pictorial in lossy compression and the main subject is compressed with
nature; the storage and communications requirements are high quality lossy compression. There are enormous
immense. Methods of compressing the data prior to storage amount of work on image compression is carried out both
and/or transmission are of significant practical and in lossless [1] [14] [17] and lossy [4] [15] compression.
commercial interest. Very few works are carried out for Hybrid Image
compression [18]-[20].
Compression techniques fall under two broad In the proposed work, for image compression, the
categories such as lossless[1] and lossy[2][3]. The former edge detection, segmentation, smoothing and dilation
is particularly useful in image archiving and allows the techniques are used. For edge detection, segmentation
image to be compressed and decompressed without losing [21],[22] smoothing and dilation, there are lots of work has
any information. And the later, provides higher levels of been carried out [2],[3]. A novel and a time efficient
data reduction but result in a less than perfect reproduction method to detect edges and segmentation used in the
of the original image. Lossy compression is useful in proposed work are described in section II, section III gives
applications such as broadcast television, a detailed description of the proposed method, the results
videoconferencing, and facsimile transmission, in which and discussion are given in section IV and the concluding
certain amount of error is an acceptable trade-off for remarks are given in section V.
increased compression performance. The foremost aim of
image compression is to reduce the number of bits needed
II. BACKGROUND
to represent an image. In lossless image compression
algorithms, the reconstructed image is identical to the A. JPEG Compression
original image. Lossless algorithms, however, are limited
Components of Image Compression System (JPEG).
by the low compression ratios they can achieve. Lossy
Image compression system consists of three closely
compression algorithms, on the other hand, are capable of
connected components namely
achieving high compression ratios. Though the
reconstructed image is not identical to the original image,  Source encoder (DCT based)

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 Quantizer The DCT based encoder can be thought of as essentially
 Entropy encoder compression of a stream of 8 X 8 blocks of image samples.
Figure 2 shows the architecture of the JPEG encoder. Each 8 X 8 block makes its way through each processing
Principles behind JPEG Compression. A common step, and yields output in compressed form into the data
characteristic of most images is that the neighboring pixels stream. Because adjacent image pixels are highly correlated,
are correlated and therefore contain redundant information. the ‘forward’ DCT (FDCT) processing step lays the
The foremost task then is to find less correlated foundation for achieving data compression by concentrating
representation of the image. Two fundamental components most of the signal in the lower spatial frequencies. For a
of compression are redundancy and irrelevancy reduction. typical 8 X 8 sample block from a typical source image,
Redundancy reduction aims at removing duplication from most of the spatial frequencies have zero or near-zero
the signal source. Irrelevancy reduction omits parts of the amplitude and need not be encoded. In principle, the DCT
signal that will not be noticed by the signal receiver, namely introduces no loss to the source image samples; it merely
the Human Visual System (HVS). The JPEG compression transforms them to a domain in which they can be more
standard (DCT based) employs the use of the discrete cosine efficiently encoded.
transform, which is applied to each 8 x 8 block of the After output from the FDCT, each of the 64 DCT
partitioned image. Compression is then achieved by coefficients is uniformly quantized in conjunction with a
performing quantization of each of those 8 x 8 coefficient carefully designed 64 – element Quantization Table. At the
blocks. decoder, the quantized values are multiplied by the
Image Transform Coding For JPEG Compression corresponding QT elements to recover the original
Algorithm. unquantized values. After quantization, all of the quantized
In the image compression algorithm, the input image is coefficients are ordered into the “zig-zag” sequence as
divided into 8-by-8 or 16-by-16 non-overlapping blocks, shown in figure 1. This ordering helps to facilitate entropy
and the two-dimensional DCT is computed for each block. encoding by placing low-frequency non-zero coefficients
The DCT coefficients are then quantized, coded, and before high-frequency coefficients. The DC coefficient,
transmitted. The JPEG receiver (or JPEG file reader) which contains a significant fraction of the total image
decodes the quantized DCT coefficients, computes the energy, is differentially encoded.
inverse two-dimensional DCT of each block, and then puts
the blocks back together into a single image. For typical AC01
DCC AC07
images, many of the DCT coefficients have values close to
zero; these coefficients can be discarded without seriously
affecting the quality of the reconstructed image. A two
dimensional DCT of an M by N matrix A is defined as
follows
M -1 N-1
 (2m +1)p  (2n +1)q
Bpq  αpαq   Amn cos cos , 0  p  M-1
m=0 n=0 2M 2N
0  q  N-1

where
 AC77
αp =  1/2/M ,, 1=p0 M-1


M p


Figure 1 Zig-Zag Sequence

αq =  1/2/N ,, 1=q0 N-1
 N q

 The JPEG decoder architecture is shown if figure 3
which is the reverse procedure described for compression.
The DCT is an invertible transformation and its inverse is
given by
B. Segmentation
M-1 N-1
 (2m +1)p  (2n +1)q Let D be a matrix of order m x n, represents the
Amn  αpαq   Bpq cos cos , 0  p  M-1
m=0 n =0 2M 2N image of width m and height n. The domain for Di,j is
0  q  N-1
[0..255] , for any i=1..m, and any j=1.. n.
The architecture of segmentation using histogram
Where is shown in figure 4. To make the process faster the high
resolution input image is down sampled 2 times. When the

αp =  1/2/M ,, 1=p0 M-1


M p

image is down sampled each time the dimension is reduced
 by half of the original dimension. So the final down sampled
m n
 image (D) is of the dimension 4 x 4 . The down sampled
αq =  1/2/N ,, 1=q0 N-1


N q
 image is smoothed to get smoothed gray scale image using

equation (1).
Si,,j = 1/9(Di-1,,j-1 +Di-1,,j +Di-1,,j+1 +Di,,j-1 +Di,,j +Di,,j+1 +Di+1,,j-1
+Di+1,,j +Di+1,,j+1 ) …(1)

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8 X 8 blocks

FDCT Quantizer Entropy Compressed
Encoder image data

Quantizer Huffman
Source image Table Table

Figure 2. JPEG Encoder Block Diagram

Compressed Entropy Dequantizer IDCT
Image data Decoder

Quantizer Huffman Reconstructed
Table Table Image

Figure 3. JPEG Decoder Block Diagram

Bi,j = (Si,j > L) and (Si,j < U) …(5)

Down Smooth the Histogram
Sample Image Computation
2 Times (D) (S) (H)

Up Binary Range
Sample Segmentation Calculation
2 Times (B) (L & U)

Figure -4 Classifier
The histogram(H) is computed for the gray scale image(S).
The most frequently present gray scale value (Mh) is
determined from the histogram by equation (2) and is shown
as indicated by a line in figure 5.
Mh = arg{max(H(x))} Figure – 5
…(2)

The background value of the images is having the highest
frequency in the case of homogenous background. In order After processing the pixel values for background area is 1 in
to surmise background textures a range of gray level the binary image B. To avoid the problem of over
values are considered for segmentation. The range is segmentation the binary image is subjected to sequence of
computed using the equations (3) and (4). morphological operations. The binary image is eroded with
L = max( Mh – 30,0) …(3) smaller circular structural element (SE) to remove smaller
segments as given in equation (6).
U = min( Mh + 30,255) …(4)
B  BSE …(6)
The gray scale image S is segmented to detect the
background area of the image using the function given in
equation (5)

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Then the resultant image is subjected to morphological 10. Quantize the DCT coefficients
closing operation with larger circular structural element as
11. Discard lower quantized values based on the
given in equation (7).
threshold value selected by the selector.
B  B  SE …(7) 12. Compress remaining DCT coefficients by Entropy
III. PROPOSED HYBRID JPEG COMPRESSION. Encoder

The input image is initially segmented into The architecture of the proposed method is shown in figure
background and foreground parts as described in section 6. The Quantization Table is a fixed classical table derived
II.B Then the image is divided into 8x8 blocks and DCT from empirical results. The Quantizer quantizes the DCT
values are computed for each block. The quantization is coefficients computed by FDCT. The classifier identifies the
performed according to the predefined quantization table. class of each pixel by segmenting the given input image.
The quantized values are then reordered based on zig-zag The selector and limiter works together to find the discard
ordering method described in section II A. The lower values threshold limit. The entropy encoder creates compressed
of AC coefficients are discarded from the zig-zag ordered code using the Code Table. The compressed image may be
list by comparing the threshold value selected by the stored or transmitted faster than the existing method.
selector as per the block’s presences identified by the IV. RESULTS AND DISCUSSION
classifier. If the block is present in foreground area then the
threshold is set to a higher value by the selector, otherwise a The Hybrid JPEG Compression method is implemented
Quantizing
Table

FDCT Quantizer
Input
Image

Classifier Selector Limiter

Code Table Entropy
Encoder

Compressed
Image

Figure 6. Hybrid JPEG compression method
lower value for threshold is set by the selector. After according to the description in section III and tested with a
discarding insignificant coefficients the remaining data are set of test images shown in figure 8. The results obtained
compressed by the standard entropy encoder based on the from the implementation of the proposed algorithms are
code table. shown in figures 7, 9,10 and table I. Figure 7.a shows the
original input image. In Figure 7.b the segmented object
Algorithm and background area is discriminated by black and white.
1. Input High Resolution Color image. The compressed bit rates of the twelve test images are
computed and tabulated in table 1. The low quality (LQ) and
2. Down sample the input image 2 times. high quality (HQ) JPEG compression is performed and the
3. Convert the down sampled image to gray scale corresponding compression ratios(CR) and PSNR values
image (G). are tabulated. The PSNR is higher for HQ and CR is higher
for LQ. The Hybrid JPEG compression performs HQ
4. Find histogram (H) of the gray scale image. compression on main subject area and LQ compression on
5. Find the lower (L) and upper (U) gray scale value background area thus the PSNR value at main subject area is
of background area. the same for Hybrid JPEG and HQ JPEG. Figure 9 shows
the comparison of normalized CRs of Hybrid JPEG and HQ
6. Find Binary segmented image (B) from the gray JPEG, it is observed that almost all of the images are
scale image (G) compressed better than classical JPEG compression. Figure
7. Up sample Binary image (B) two times. 10 shows how well the compression ratio is increased than
the classical JPEG compression method.
8. Divide the input image into 8x8 blocks
9. Find DCT coefficients for each blocks

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a)Input Image

b)Segmented Main Subject Area
Figure – 7 Input /Output

Table -1 Compression Ratio and PSNR values obtained by Hybrid JPEG and JPEG

Image LQ Hybrid Hybrid/HQ HQ
CR1 PSNR1 CR2 PSNR2 PSNR@ CR3 PSNR3 CR2-CR3
MainSubject
3 26.00 21.52 7.46 27.82 27.84 7.46 27.82 0.0000
5 25.29 22.09 6.80 28.87 28.93 6.80 28.87 0.0004
10 23.83 20.78 5.41 28.09 28.13 5.41 28.09 0.0031
4 24.33 22.08 6.61 31.13 31.20 6.60 31.13 0.0068
11 27.75 25.33 8.62 35.97 36.25 8.61 36.01 0.0198
9 24.92 21.62 6.58 30.45 30.56 6.56 30.51 0.0204
1 27.54 23.03 8.40 29.37 29.40 8.37 29.37 0.0276
7 24.85 17.71 4.01 23.38 23.43 3.92 23.38 0.0911
8 24.63 21.97 6.73 31.05 31.13 6.64 31.10 0.0921
12 29.18 22.73 5.92 28.70 29.07 5.61 28.93 0.3111
6 26.47 20.12 5.54 25.31 25.33 5.19 25.30 0.3449
2 22.84 20.40 7.91 27.93 28.21 7.25 28.06 0.6541

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Figure – 8 Test Images (1-12 from Left to Right and Top to Bottom)

1.2

1
Normalized CR

0.8
HyBrid
0.6
HQ Lossy
0.4

0.2

0
1 2 3 4 5 6 7 8 9 10 11 12
Images

Figure – 9 Normalized Compression Ratio Obtained for Test Images
Increased Compression Ratio by Hybrid JPEG Compression

0.7

0.6

0.5
Compression Ratio

0.4

0.3

0.2

0.1

0
1 2 3 4 5 6 7 8 9 10 11 12
Images

Figure – 10 Increased Compression Ratios by Hybrid Compression.

V. CONCLUSION the background area is lower in Hybrid JPEG method which
is acceptable, since the background area is not vital. The
The compression ratio of Hybrid JPEG method is
Hybrid JPEG method is suitable for imagery with larger
higher than JPEG method in more than 90% of test cases. In
trivial background and certain level of loss is permissible.
the worst case both Hybrid JPEG and JPEG method gives
the same compression ratio. The PSNR value at the main
subject area is same for both methods. The PSNR value at

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ACKNOWLEDGEMENTS Exploring the Space of Realistic Distortions”, IEEE Transactions
on Image Processing, vol. 17, no. 8, pp. 1261–1273, Aug 2008.
The authors express their gratitude to University [20] Hong, S. W. Bao, P., “Hybrid image compression model based
Grant Commission and Manonmanium Sundaranar on subband coding and edge-preserving regularization”, Vision,
Image and Signal Processing, IEE Proceedings, Volume: 147,
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