International Journal of Engineering Trends and Technology (IJETT) – Volume23 Number 4- May 2015
Hybrid Image
Compression Technique for ROI based compression
Kuldip K. Ade*, M. V. Raghunadh΅
Dept. of Electronics and Communication Engineering
National Institute of Technology
Warangal-506004, India
Abstract— This paper presents the hybrid method for
compression of images. In any image region of interest is the
significant part, which should be compressed with high PSNR
while the rest of the image can be compressed with low PSNR for
achieving a good compression ratio. The method proposed in this
paper separates the face of the person from the image (Images
with a human face as ROI) and compress it with visually lossless
wavelet based image compression and the rest of the image with
DCT based JPEG compression. Areas, corresponding to ROI are
compressed for maximum reconstruction quality, while
remaining areas are coarsely approximated. The paper also
proposes the face detection technique for automatic ROI selection
based on skin color detection and dimensions of the human face.
This method achieves better PSNR for ROI while providing a
remarkable compression ratio for image.
Keywords—ROI (Region of interest); PSNR (peak signal to
noise ratio); CR (Compression ratio); Huffman coding; Face
detection.
I. INTRODUCTION
Region of interest oriented image compression mainly
focused on viewer’s interest. It is a type of compression that
allocates a given amount of information to a region in an
image where a viewer has more interest, (ROI) [1] more
preferred than the remaining region, which is non-ROI. This
approach results in higher restored image quality for ROI than
that for non-ROI. This prevents an unallowable loss of the
information for most important regions such as the tumor in
Brain is the focus in the medical imaging [2]. ROI image
compression is an interesting topic in the field of image
compression. In various image compression standards, the
whole image is compressed either by lossless compression or
by lossy compression. These image compression standards
treats the ROI and non ROI equally which results in the loss of
information related to the highly desirable areas. Information
related to the region of interest in an image can be preserved
by compressing the region with either lossless or visually
lossless method of image compression and the region other
than region of interest should be compressed by lossy
compression which helps in achieving better compression for
whole image. In images like voter Identity cards, Adhar card
and images with a human face, the main focus of the image is
the face of the person. The quality of such image judged by
viewer depends on the quality of reconstructed ROI (human
face in this context). It will be appropriate to use either
lossless or compression that appears to be lossless for face. If
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lossless or near lossless compression is used, then the
compression ratio achieved will be very less. Compression
ratio should be significant to handle the large database. This
problem can be solved by using lossy compression with high
CR for the rest of the image. This technique not only helps in
achieving good quality for ROI [3] but also provides better CR
for full image transmission or storage.
Image compression algorithms used nowadays, determines
ROI region manually before image transmission, which is time
consuming when processing a large database containing
number of images. Therefore, people need to solve the batch
processing of the image ROI extraction problem. This paper
deals with separation of ROI with simple and fast face
detection technique, which serves as the best method to
compress the large database of voter Identity cards or Adhar
cards. This algorithm will solve the issue of handling large
database by providing good compression at the same time it
will preserve the quality of an image by providing a good
PSNR for ROI [4]. It ensures no loss of important information
at the same time; it can effectively compress the data.
Compression algorithm proposed in this paper appears to be
lossless for ROI region, but it is lossy. This algorithm is
designed to achieve a good CR, with the appearance of the
reconstructed ROI region in the image will be same as
original. Hence, as compared to other lossy compression
techniques it resembles the characteristics of lossless
technique. The percentage of loss by proposed algorithm is
less as compared to other lossy compression techniques.
Algorithm explained in this paper detects the face of a
person from the color image and form a mask to separate the
image in ROI and non ROI image. ROI is then compressed by
wavelet transform based compression [5] and non ROI region
is compressed with DCT based JPEG image compression [6].
Stream of transformed and quantized pixels from both the
region are combined into a single stream which is then
preceded for Huffman coding. Reconstruction (decoding)
procedure is exactly reverse of the encoding. This technique
serves as the best in both aspect of achieving better CR and a
good PSNR for ROI as compared to the individual wavelet
based compression or DCT based JPEG compression. This
algorithm is also adaptable for other near lossless compression
techniques in ROI region and lossy compression techniques in
the non ROI region.
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International Journal of Engineering Trends and Technology (IJETT) – Volume23 Number 4- May 2015
Condition1:
II. ROI SELECTION
10/7 RegionHeig ht RegionWidt h
The method used for face detection [7] [8]in this paper is
(4)
based on skin color filtering, but it has some limitations. These
3/10 RegionHeig ht
limitations can be compensated by using other filtering
methods simultaneously with skin color filtering [9]. While
Condition 2:
detecting face by skin color, face is not only object that could
be found in an image, also neck, arms, legs, and palms will
1.6 RegionWidt h RegionHeig ht
(5)
found by the skin color filtration method. This issue can be
1.19 RegionWidt h
solved by applying the criteria of height to width ratio [10] of
the standard human face to sort out face region from skin color
detected regions [9].
Step 8. Segment the face from an image by using obtained
mask.
A. Algorithm to detect face
Step 1. Input the image.
III. COMPRESSION BASED ON WAVELET TRANSFORM
Step 2. Find out the normalized RGB image (rgb(i, j)) and
HSV image (HSV(i, j)) from the input image.
Step 3. For each pixel (i,j), get the corresponding normalized
r and g value, also H and S values.
Step 4. Fix the threshold for r, g, H, S to get the mask for
separating the skin region from the image.
Mask 10 H(i, j) 0.5934 or
(1)
6.0563 H(i, j) 6.2657
Mask 2-
0.2 S(i, j)
0.757
(2)
Mask 3-
0.4 r(i, j)
g(i, j)
Algorithm explained in this paper uses part of compression
based on discrete wavelet transform for ROI. A wavelet, in the
sense of the Discrete Wavelet Transform [5] is an orthogonal
function which can be applied to a finite group of data. The
wavelet basis is a set of functions which are defined by a
recursive difference equation,
M 1
(x)
k 0
k
1 - r(i, j)
and
2
(3)
(6)
c j (k)
j , k (t)
n jk
dn (k)
n, k (t)
(7)
Where, the coefficients in the wavelet expansion series c j (k)
and dn (k) are called as the discrete wavelet transform of the
g(i, j) r(i, j)
function f (t) .
j , k (t)
Step 5. Multiply these three masks to get the final mask.
Step 6. Perform Binary Mask Post-Processing
This can be achieved by performing morphological
operations on the mask image, which removes holes
and unnecessary regions in the mask and also
eliminates some small masked regions. The
operations to be carried out on the image are erosion
followed by the dilation.
Step 7. Image Segmentation on the basis of Shape Based
Filtering
This processed mask image is segmented using a
connected component labeling. Segmentation to be
carried out has to pass the test of shape based feature
which is an aspect ratio of width to height of the
region.
Conditions that to be passed, for the region to be
recognized as face region are as follows:
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k)
Where, the range of the summation is determined by the
specified number of nonzero coefficients M.
Any function f(t) belonging to the L2(R) space can be
represented in wavelet expansion series in terms of the scaling
function as follows:
f (t)
0.6 and
ck (2 x
and
n, k (t)
are defined as follows,
j , k (t)
2 j /2 (2 j t k)
(8)
n, k (t)
2n/2 (2n t k)
(9)
Further, c j (k) and dn (k) are described by following formulae
which are basic of wavelet based filtering, which is defined as
a combination of a low pass filter and high pass filter, both
followed by a factor of two decimation.
c j (k)
d j (k)
n
n
h(n 2 k)c j 1 (n)
(10)
g(n 2 k)c j 1 (n)
(11)
This can be explained by following diagram.
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International Journal of Engineering Trends and Technology (IJETT) – Volume23 Number 4- May 2015
c j-2
h
After knowing the basics of DWT, the method of image
compression based on DWT can be easily understood by the
figure given below.
2
c j-1
h
2
dj-2
cj
h
DWT based Im age
com pression steps
g
2
2
Tile
Decomposition
dj-1
g
c j+1
g
2
DWT
2
Compressed
Image data
Source
Image
dj
DWT based Im age
Decom pression steps
Fig. 1 Three stage analysis sub band coding.
An image is represented as an array of coefficients, which
are brightness intensity of the pixels. The image consists of
smooth variation, termed as low frequency variations and the
sharp edges as high frequency variations. Discrete Wavelet
Transform (DWT) is used to separate smooth variations and
the details of the image. In DWT analysis filter pair consists of
a low pass filter and a high pass filter as shown in fig. The low
pass and high pass filter are so chosen that they exactly halve
the frequency range between them. This process is explained
in Fig. 2. Scheme of decomposition of image can be best
explained by following figure.
LL2
LH2
LL1
HL2
HH2
LH1
Original
HL1
Tile
Integration
IDWT
Dequantizer
Fig. 4 Wavelet based image compression and decompression.
IV. DCT BASED JPEG COMPRESSION
DCT-based JPEG compression [6] (without entropy
encoding) is used for the compression of non ROI in an image. It
will be appropriate to go through the basics of this compression
technique.
A. 8x8 FDCT and IDCT
At the time of encoding, source image samples are
grouped into 8x8 blocks, shifted from unsigned integers with
range [0, 2p – 1] to signed integers with a range [-2(p-1), 2(p-1)-1]
and input to the Forward DCT (FDCT). At the decoder side,
the Inverse DCT (IDCT) outputs 8x8 sample blocks to form
the reconstructed image. The following equations are the
mathematical definitions of the 8x8 FDCT and 8x8 IDCT:
7
7
x=0 y=0
cos
LL1
HL1
(2y +1)vπ
16
HH1
f(x, y) =
1
4
7
7
c(u) c(v)F(u,v)cos
x=0 y=0
cos
LL2 LH2
LH1
HLL1
HL1
(2u +1)xπ
16
(2v +1)yπ
16
LH1
HL2 HH2
HH1
HL1
HH1
Where; c(u),c(v) =
1
; for
2
u, v = 0 ;
Fig. 3 Scheme of decomposition up to second level
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(12)
LH1
Original Image
H1
(2x +1)uπ
16
f(x, y)cos
Fig. 2 Scheme of decomposition
L1
Entropy
Decoder
Compressed
Image data
Reconstructed
Image
1
F(u, v) = c(u) c(v)
4
HH1
LLL1
Entropy
Encoder
Quantizer
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International Journal of Engineering Trends and Technology (IJETT) – Volume23 Number 4- May 2015
Step 5. Apply the wavelet based image compression on ROI
Otherwise; c(u),c(v) =1 ;
to obtain the stream or vector of data obtained after
quantization without entropy encoding (Process M1).
Procedure of JPEG image compression based on DCT can be
explained by the figure given below.
Step 6. Combine both the stream of quantized pixel values
obtained from both compression techniques.
DCT based JPEG Image
compression steps
8x8
blocks
FDCT
Source
Image
Table
Specifications
Quantizer
Step 7. Apply Huffman encoding on this data stream to
obtain a sequence of variable length codes.
Entropy
Encoder
Compressed
Image data
Table
Specifications
DCT based JPEG Image
Decompression steps
IDCT
Reconstructed
Image
Table
Specifications
Dequantizer
Table
Specifications
B. Decompression
Step 1. Apply Huffman decoding procedure to obtain the
symbols (pixel values) provided as input to Huffman
encoding.
Step 2. Separate the stream of pixel values obtained from
Huffman decoding in to ROI and non ROI stream of
pixel values.
Entropy
Decoder
Compressed
Image data
Fig. 5 DCT based JPEG image compression and decompression.
V. HUFFMAN ENCODING
Huffman code [11] is a variable-length code. The variable
length code assigns codes which are of variable length to
symbols to be encoded. Huffman coding provides a way to
encode the symbols in a lossless way of compression.
Huffman codes are widely used lossless image compression
technique.
Step 3. Apply the DCT based JPEG image decompression
(without entropy decoding) on non ROI stream to
obtain non ROI (Process Inverse M2).
Step 4. Apply the wavelet based image decompression
(without entropy decoding) on ROI stream to obtain
ROI (Process Inverse M1).
Step 5. Combine ROI and non ROI to get the final
reconstructed image.
Step 6. Output is reconstructed image with preserved ROI
with better CR.
The Huffman code procedure is based on the two
implications.
ROI
Huffman
Encoding
Face
detection
1) Symbols with higher frequency will have shorter
code words than symbol that occur less frequently.
2) The two symbols those occur least frequently will
have the same length.
M1
Non
ROI
Source
Image
M2
Compressed
bit stream
VI. ALGORITHM
Inverse
M1
A. Compression
Step 1. Input the image having human face.
ROI bit
stream
Huffman
Decoding
Step 2. Send the image for face detection and get the mask of
face detected region.
Reconstructed
Image
Inverse
M2
Non
ROI bit
stream
Compressed
bit stream
Fig. 6 Implemented Hybrid compression and decompression algorithm
Step 3. Segment the image in ROI and a non ROI image
using the mask obtained from step2.
VII. EXPERIMENTAL RESULTS
Step 4. Apply the DCT based JPEG image compression on
non ROI to obtain the stream or vector of data
obtained after quantization without entropy encoding
(Process M2).
ISSN: 2231-5381
The algorithm is operated on color images having human
face. A database of images has been operated are Adhar card,
Identification cards, election commission Id cards and other
images with a human face in it. Performance of discussing
compression technique is tested on basis of CR and PSNR.
PSNR and CR obtained by this hybrid approach [12] are
compared with PSNR and CR obtained by individual methods
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International Journal of Engineering Trends and Technology (IJETT) – Volume23 Number 4- May 2015
which are used for a hybrid approach. Compression ratio
or with technique providing good visual quality of the image
criteria are tested on whole images, while PSNR for this
at the same time good compression ratio is achieved for image
method is compared on the basis of PSNR of ROI and non
compression. The proposed algorithm provides good picture
ROI. In this method ROI is compressed relatively lossless and
quality even at high compression. ROI is kept visually
the rest of the image compressed in a lossy manner. Hence
lossless; hence PSNR for ROI is high and non ROI is
PSNR of ROI and non ROI is calculated separately.
compressed by lossy compression, which helps in achieving a
Experimental results show that, good CR can be achieved by
better compression ratio. This algorithm can be used in many
compromising PSNR of non ROI, while maintaining highest
applications [16]depending on the criteria of selection of ROI.
quality of ROI (here in this context face of the person). Table I
Medical images found to be best suitable for this algorithm, if
shows that Hybrid image compression technique [13]
ROI selection is based on unwanted or forensic materials in
providing nearly equal CR as that of DCT based JPEG image
the human body.
compression, while maintaining better PSNR for ROI region.
Compression achieved by this approach is much higher than
wavelet based image compression, maintaining comparable
PSNR for ROI area.
Peak Signal to noise ratio (PSNR) is calculated by
following formulae,
m n
MSE
i 1j
PSNR
[ X (i, j ) Y (i, j )]2
m n
1
(14)
(a) DCT based JPEG compression
(b) Wavelet based compression
(15)
20 log10 [255 / MSE ]
Where,
X(i, j) = Original image;
(c) Hybrid approach for image compression
Y(i, j) = Recovered image after decompression;
Fig. 7 Reconstructed images of Adhar card for different image compression
methods.
m and n are height and width of the image;
MSE = Mean Square Error;
Compression ratio (CR) is calculated by following formulae,
Compression ratio
Original image size
Compressed image size
(15)
Values of CR and PSNR are calculated for different
images using DCT based JPEG compression, Wavelet based
image compression [14] and for Hybrid image compression
discussed in this paper. PSNR obtained by Hybrid method of
compression for ROI, is better than both the individual
methods. PSNR for non ROI has nearly same as that of PSNR
obtained by DCT based JPEG image compression also the CR
obtained is remarkable and comparable to DCT based JPEG
compression method. This signifies that a hybrid approach is
providing the benefits of both individual methods (a good
PSNR from DWT based compression and good CR from DCT
based JPEG compression method). The average of the
compression ratio and Average of PSNR (for ROI) for images
operated under this algorithm is found to be 15.3499 and
41.1862 respectively.
(a) DCT based JPEG compression
(b) Wavelet based compression
(c) Hybrid approach for image compression
Fig. 9 Reconstructed images of Canada PM for different image compression
methods.
VIII. CONCLUSION
The algorithm proposed in this paper, Hybrid Image
Compression Technique for ROI [15] [13] based compression.
The algorithm provides a better way to compress the image on
the basis of priority of regions. In this approach ROI is
compressed with relatively less lossy compression technique
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International Journal of Engineering Trends and Technology (IJETT) – Volume23 Number 4- May 2015
digital still images," in Image Processing and Communications
Challenges 3. Springer, 2011, pp. 59-64.
[8] H. H. K. Tin, "Robust Algorithm for Face Detection in Color Images,"
International Journal of Modern Education and Computer Science
(IJMECS), vol. 4, no. 2, p. 31, 2012.
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skin color and eyes," in Multimedia and Expo, 2000. ICME 2000. 2000
IEEE International Conference on, vol. 1, 2000, p. 133–136.
(a) ROI for Adhar card image
(b) ROI for Canada PM image
[10] K. Sandeep and A. N. Rajagopalan, "Human Face Detection in Cluttered
Color Images Using Skin Color, Edge Information.," in ICVGIP, 2002.
Fig. 8 Region inside the red box is the ROI and the rest of the image is non
ROI.
[11] R. C. Gonzalez, R. E. Woods, and S. L. Eddins, "Digital image
processing using MATLAB," Upper Saddle River, N. J: Pearson
Prentice Hall, 2004.
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TABLE I
COMPARISON OF HYBRID IMAGE COMPRESSION ON THE BASIS OF PSNR AND CR
IMAGES
PSNR
(By Hybrid
approach for
ROI)
PSNR
(By Hybrid
approach for
non ROI)
PSNR
(By DCT
based JPEG
comp. on
whole image)
PSNR
(By DWT
based comp.
on whole
image)
CR
(By Hybrid
approach)
CR
(By DCT
based JPEG
comp.
approach)
CR
(By DWT
based comp.)
Canada PM
40.4023
30.4477
13.0719
33.59261
20.9628
29.9474
15.7079
Election ID
42.1817
21.2049
23.0197
28.1238
12.0531
13.3337
3.8176
Adhar card
45.5398
23.7561
20.6229
30.3115
16.6520
18.7335
5.1684
Company ID
40.1242
25.4295
19.3462
31.3405
12.3700
14.7108
7.4426
President
37.6601
27.6090
16.2533
31.7178
14.7116
21.8770
9.6139
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