This document proposes a hybrid IBTC-DWT encoding scheme that combines the simple computation and edge preservation of interpolative block truncation coding (IBTC) with the high compression ratio of discrete wavelet transform (DWT). Simulation results showed that the proposed algorithm achieved better performance than IBTC-DCT in terms of compression ratio, bit rate, and reconstruction quality at low bit rates. The hybrid approach reduces computational complexity by applying DWT to the smaller sub-images produced by IBTC.

1. IBTC-DWT Hybrid Coding of Digital
Images
Authors:
Ali Abdulhafid Elrowayati, Zakaria Suliman Zubi,
Electronic Engineering Department, Computer Science Department,
The College of Industrial Technology, Faculty of Science
Misurata, Libya Sirte University
Sirte, Libya
02/07/13 1

2. Outline
► Abstract
► Introduction
► Theproposed scheme
► Simulation results
► Conclusion
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3. Abstract
1. A hybrid IBTC-DWT encoding combines the simple
computation and edge preservation prosperities of
interpolative block truncation coding(IBTC) and high
compression ratio of discrete wavelet transform(DWT).
2. This implemented yields significantly lower coding delay
than DWT alone, and to achieve a reduced bit rate is also
proposed and investigated.
3. In this hybrid IBTC-DWT algorithm, the resulting high-
means and low-means sub images from IBTC algorithm
are coded using DWT transform.
4. Simulation results showed that good performance was
demonstrated in terms of compression ratio, bit rate and
reconstruction quality.
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4. Outline
► Abstract
► Introduction
► Theproposed scheme
► Simulation results
► Conclusion
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5. Introduction
► Image Coding
 Compression of digital images
has been a topic of research for
many years and a number of
image compression standards
has been created for different
applications.
 The role of compression is to
reduce bandwidth requirements
for transmission and memory
requirements for storage of all
forms of data.
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6. Introduction
► There are two main families for image compression:
 Lossless image compression techniques
► Lossless have the disadvantage of being limited in term of
compression rate.
 Lossy techniques
► Lossy techniques allow larger compression rates.
► while introducing some distortion in reconstructed images.
► In order to improve compression rates, we are interested in the
second family of techniques.
► In this paper, we propose a novel method of encoding an image
using both the interpolative block truncation coding (IBTC) and
discrete wavelet transform (DWT) to achieve significant
improvement in digital image compression performance.
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7. Introduction
► Block Truncation Coding (BTC)
 BTC is a block-based lossy image compression
 First developed in 1979 for grey scale image
coding
 The output data of BTC for an image block
contains one bitmap and two quantization
levels
 BTC has very few computations, edge-
preserving ability; but only a medium
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compression ratio. 7

8. An Example of BTC Encoding
w
4×4 image pixels Bitmap
140 142 144 145 0 0 1 1
146 141 146 142 1 0 1 0
145 141 144 142 1 0 1 0
142 138 141 144 0 0 0 1
Mean value X=142.5 # of 0 is 9
Original Image
Variance value ρ=2.199 # of 1 is 7, q=7
q 7
X L = X −σ = 142.5 − 2.199 * = 141
m−q 9
Two quantization levels
m−q 9
XH = X +σ = 142.5 + 2.199 * = 145
q 7
8

9. An Example of BTC Decoding
Bitmap
0 0 1 1 141 141 145 145
1 0 1 0 145 141 145 141
Decoding
1 0 1 0 145 141 145 141
0 0 0 1  X L , bi = 0, 141 141 141 145
oi = 
ˆ
 X H , bi = 1.
X L = 141 X H = 145 Reconstructed pixels
9

10. Introduction
► INTERPOLATIVE Block Truncation Coding
(IBTC)
 IBTC algorithms are based on the fact of the
adjacent image pixels have high degree of
correlation and the resulting bit-maps will also high
degree of correlation.
 Only half of the bits of bit maps for each block are
transmitted or stored and the other are interpolated.
 IBTC uses only 8 bits of 4× 4 bit-maps instead of
16 bits, thereby reducing the bit rate from 2
bits/pixel to 1.5 bits/pixel.
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11. Introduction
► Discrete wavelet transform (DWT)
 DWT can be efficiently used in image coding
applications because of its data reduction capabilities.
 Unlike the case of Discrete Cosine Transform (DCT)
which based on cosine functions, DWT has some
properties, making it a better choice for image
compression than DCT, especially for image on higher
resolutions.
 DWT coding gives better representation of bits with
localization in both the spatial and frequency domains
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12. Introduction
► The main idea of the proposed method:
 The presented hybrid IBTC-DWT algorithm
combines the simple computation and edge
preservation prosperities of IBTC and high
compression ratio of DWT
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13. Outline
► Abstract
► Introduction
► Theproposed scheme
► Simulation results
► Conclusion
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14. The proposed scheme
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Fig 1 System diagram

15. The proposed scheme
► For a 512×512 input images with 4×4 blocks
► the sub sampled images are 128×128 in size.
 The sub sampled-images have details and features which
must be preserved, since any distortion involved here will
be distorted over all of the pixel in each reconstructed IBTC
block.
 DWT is directly implemented on both the high-mean sub
image and the low-mean sub image.
 For example, when using level-2 of decomposition, and
take the important coefficients with high energy.
 Since the size of sub images is relatively small (16 times
less than original image) the computational complexity is
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reduced.

16. Outline
► Abstract
► Introduction
► Theproposed scheme
► Simulation results
► Conclusion
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17. Simulation results
► Test image : Lenna ,size is 512×512
bit resolution is 8 bit
► DWT transform has been used with different
scalar quantization, where the significance
of coefficients are directly related to its
magnitude as well as their sub bands after
wavelet decomposition at different low bit
rates. (0.82 bpp as example)
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18. Table 1. IBTC-DCT and IBTC-DWT hybrid coding results.
IBTC-DCT IBTC-DWT
Image
PSNR
PSNR(dB) Bitrate(bpp) (bpp)
dB
Lena 30.50 0.82 32.14 0.82
Cameraman 28.25 0.82 30.29 0.82
Pepper 29.00 0.82 30.00 0.82
Chest 28.50 0.82 29.50 0.82
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19. Simulation results
► In previous table show that the proposed
algorithm give better performance in terms
of The MSE and PSNR compared to the
result of IBTC-DCT algorithm in [5].
► In Fig.1, shows one of the test images and
its reconstructed version using the proposed
algorithm at different low bit rates.
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20. Simulation results
Fig.1. The original
and reconstructed
image using
different bitrate.
(a) original Lena.
(b) Reconstructed
using 0.102bbp
(c) Reconstructed
using 0.50bbp
(d) Reconstructed
using 0.820bbp.

21. Outline
► Abstract
► Introduction
► Theproposed scheme
► Simulation results
► Conclusion
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22. Conclusion
► Digital Image Compression has been achieved using the
proposed IBTC-DWT algorithm.
► Comparison between the numerical results obtained by
proposed algorithm with the corresponding ones obtained
by of IBTC-DCT algorithm in [5].
► This IBTC-DWT algorithm gives good quality reconstructed
images at low bit rate.
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23. Thanks~
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