ATFECM-30109054

Asian Transactions on Fundamentals of Electronics, Communication & Multimedia (ATFECM) (ATFECM ISSN: 2221-4305) Volume 01 Issue 05

Nov 2011 ATFECM-30109054Asian-Transactions 1

Abstract A well known image compression algorithm named Set Partitioning in Hierarchical Trees (SPIHT) is an efficient

wavelet-based progressive image-compression technique,

designed to minimize the mean-squared error (MSE) between the

original and decoded imagery. Since wireless channels suffer from

significant bit error rates, some mechanism to protect the encoded

image required. Without such a mechanism, the channel bit

errors will prevent accurate decoding of the image. Many error

control solution have been proposed to address the problem of

transmitting images over wireless channels. These error control

coding (channel coding) techniques add redundancy at the source

level. Asymmetric modulation method, Hierarchical Quadrature

Amplitude Modulation (HQAM) provides alternative means of

Unequal error protection to protect the transmitted sensitive

coded bits without increasing the bandwidth. The paper proved

that HQAM method can reduced sensitive bit error when using

SPHIT image encoding where SPHIT has a drawbacks on error

resilience. The performance of SPIHT coded image transmission

is evaluated using gray test image and for different values of the

modulation parameter and different values of bpp (bit-per-pixel).

Index Terms Error protection, HQAM, Image compression,

SPIHT Image coding, Wireless channel.

I. INTRODUCTION

any techniques have been proposed for the transmission

of images over wireless networks. One obvious

technique is to take an existing image encoder, and then

protect the bit stream using convolution codes a separate

source-channel coding scheme. New algorithms for image

compression based on wavelets have been recently developed

[1-4]. These methods have resulted in practical advances such

as: superior low-bit rate performance, continuous-tone and bit-

level compression, lossless and lossy compression, progressive

Manuscript received October 7, 2011. This work was supported by the

Ministry of Science, Technology and Innovation (MOSTI) under grant 01-01-

15-SF0115.

Md. Abdul Kader is with the School of Computer and Communication

Engineering, University Malaysia Perlis, Malaysia. (e-mail:

[email protected]).

Prof. Dr. Farid Ghani is with the School of Computer and Communication

Engineering, University Malaysia Perlis, Malaysia. (e-

mail:[email protected]).

Assoc. Prof. R. Badlishah Ahmed is with the School of Computer and

Communication Engineering, University Malaysia Perlis, Malaysia. (e-mail:

badli@@unimap.edu.my).

transmission by pixel accuracy and resolution, region-of-

interest coding and others. One of the most efficient

procedures that fulfil the above goals is the Set Partitioning in Hierarchical Trees (SPIHT) algorithm [1]. It was introduced

by Said and Pearlman [1, 5]. Some of the best results are

highest PSNR values for given compression ratios and for a

wide variety of images have been obtained with SPIHT.

Consequently, it is probably the most widely used wavelet-

based algorithm for image compression, providing a basic

standard of comparison for all subsequent algorithms.

Although the SPIHT coder is a progressive encoding in which

the decoding can be terminated at any bit in the stream, it has

very poor error resilience (single bit errors often cause a loss

of synchronization between the encoder and decoder and can

result in a badly distorted image). Therefore, if channel

included bit errors, decoding can be terminated before the bit

containing the error is decided. The error is likely to cause

later bits to be decided incorrectly as well, which significantly

increases decoding error.

Hierarchical modulation methods provide alternative ways

for unequal error protection (UEP) to the transmitted bits

without increasing the bandwidth. The most commonly used

hierarchical modulation method is Hierarchical Quadrature

Amplitude Modulation (HQAM). This modulation technique

assigns unequal protection to all the transmitted bits, hence is

classified as unequal error protection (UEP) method of

modulation [6]. HQAM technique is a modification of

Quadrature Amplitude Modulation (QAM) technique where

QAM provides equal error protection (EEP) to all the

transmitted bits. Thus UEP modulation methods are to be

preferred particularly for coded image data transmission, as

these will allow gradual protection of the data with regard to

its importance i.e. more important data will get a stronger

protection than less important. This is a simple and efficient

approach in which non-uniform signal-constellation is used to

give different degrees of protection. The advantage of this

method is that different degrees of protection are achieved

without an increase in bandwidth; in contrast to channel

coding that increases the data rate by adding redundancy [7-

11].

In this paper an image transmission system has been

proposed where Set Partitioning in Hierarchical Tree (SPIHT)

Unequal Error Protection for SPIHT Coded

Image Transmission over Erroneous Wireless

Channels

Md. Abdul Kader, Farid Ghani and R. Badlishah Ahmed

M



algorithm is used as an image coder and 16-HQAM technique

is used for transmission of the coded image over wireless

erroneous (AWGN and Fading) channels. PSNR analysis is

presented for the proposed image transmission system and the

performance comparisons are carried out through computer

simulation with gray image as test image for different values of

modulation parameter and different value of bpp (bit-per-

pixel).

This paper has been organized as follows: In section II,

general model of image transmission system is given. Section

III considers an overview of SPIHT image compression

technique. In section IV overview of Hierarchical 16-QAM

describes. Based on computer simulation results, the

performance of HQAM for the transmission of SPIHT coded

images is considered in Section V.

II. MODEL OF IMAGE TRANSMISSION SYSTEM

The essentials of the image transmission system considered

here are shown in Fig. 1. The source encoder encodes the

source image using appropriate image compression technique.

For the protection of coded image in Fig. 1 channel encoder

add redundancy to the coded image by using appropriate

channel coding technique. Modulator modulates the coded

image and transmits through wireless channel. QAM is

invariably used as the modulation technique [13, 14]. The

channel introduces noise and distortion to the transmitted

image. The demodulator receives the image data with error

and demodulates it. After channel decoding, the coded image

is decompressed.

Fig. 1. Model of image transmission system

III. OVERVIEW OF SPIHT ALGORITHM

SPIHT stands for Set Partitioning in Hierarchical Trees. The

term hierarchical trees refers to the quadtrees. Set partitioning

refers to the way these quadtrees partition the wavelet

transform values at a given threshold.

The SPIHT algorithm is based on hierarchical set

partitioning, which can be thought of as a divide-and-conquer

strategy. The SPIHT algorithm views wavelet coefficients as a

collection of spatial orientation trees, with each tree consisting

of coefficients from all subbands that correspond to the same

spatial location in an image. A partitioning rule is used to

divide a given set into smaller subsets so that significant

coefficient can be efficiently isolated.

The SPIHT consists of two main stages namely sorting and

refinement. For practical implementation, SPIHT maintains

three linked lists viz. the list of insignificant pixels (LIP), the

list of significant pixels (LSP) and the list of insignificant sets

(LIS). At the initialization stage, SPIHT initializes the LIP

with all the pixels in the highest level of the pyramid (i.e. LL

subband), the LIS with all the coefficients at the highest level

of the pyramid except those, which don't have descendents,

and LSP as an empty set. During the sorting pass, the

algorithm first traverses through the LIP, testing the magnitude

of its elements against the current threshold and representing

their significance by 0 or 1. Whenever a coefficient is found

significant, its sign is coded and it is moved to LSP. The

algorithm then examines the LIS and performs a magnitude

check on all coefficient of set. If a particular tree/set is found

to be significant, it is partitioned into its subsets (children and

grandchildren) and tested for significance. Otherwise a single

bit is appended to the bit stream to indicate an insignificant set

(or zero-tree). After each sorting pass SPIHT outputs

refinement bits at the current level of bit significance of those

pixels which had been moved to LSP at higher threshold,

resulting in the refinement of significant pixels with bits that

reduce maximum error. This process continues by decreasing

current threshold by factor of two until desired bit rate is

achieved.

The SPIHT coder generates different type of bits that have

different degree of vulnerability to the errors. The effect of

error in some bits is more severe, damaging the image globally

by disturbing the synchronization between the encoder and

decoder. On the other hand, some bits are less sensitive to

errors, and damage the image locally without disturbing the

synchronization.

Based on the degree of their sensitivity, the bits generated

by SPIHT algorithm can be classified into two classes Critical

Bits (CB) and Non-Critical Bits (NCB). The critical bits are

those, which causes the loss of synchronization between the

encoder and decoder. A single bit error in critical bit causes

the failure of the reconstruction process after that point. It

consists of significant bits generated during LIP and LIS tests.

The non-critical bits on the other hand cause less severe errors.

The effect of error in a noncritical bit is limited to a single

coefficient and does not disturb the progression of the

decoding process after it occurs. The non-critical bits consist

of the sign and refinement bits.

IV. HIERARCHICAL 16-QAM

Hierarchical Quadrature Amplitude Modulation (HQAM) is

more spectrally efficient and dc-free modulation scheme.

Hierarchical transmission system is composed of a hierarchical

source coder and the corresponding channel coder divides the

information into several layers according to their significance,

and transmits each layer with different reliability according to

the layers.

Fading and

Noise Channel

with Errors

Received Data

with Error

Compressed

Data

Transmitted

Image

Channel

Encoder Modulator

Source

Encoder

Compression

De-

Modulator

Channel

Decoder Received

Image

Source

Decoder

De-Compression



The basic idea behind hierarchical modulation consists of

the partitioning the coded image data stream into two parts: the

significant or high-priority (HP) information and the non-

significant/refinement or low-priority (LP) information.

After channel encoding, the HP and LP information are de-

multiplexed into a single stream and mapped on non-uniformly

spaced constellation points creating different levels of error

protection [12]. In this way, HQAM provides a way of

Unequal Error Protection (UEP) to the transmitted data bits, in

which the high priority data bits (HP) of the coded image are

mapped to the Most Significant Bits (MSB) of the modulation

constellation points while the low priority data bits (LP) of the

coded image are mapped to the Least Significant Bits (LSB) of

the modulation constellation points [10]. Unequal error

protection (UEP) modulation methods are to be preferred

particularly for image data transmission, as these will allow

gradual protection to the transmitted data with regard to its

importance.

Using HQAM will, therefore, result in improved image

quality at low channel signal to noise ratio (SNR) conditions,

since the highly sensitive HP data bits are mapped to the

MSBs of the HQAM with low bit error rate (BER). However,

for the sake of simplicity only 16-HQAM is considered in this

paper.

In Hierarchical QAM, it is possible to give the higher

protection to the most important data (significant bits) by

changing the value of modulation parameter . is the ratio of

the distance b between quadrants to the distance c between the

points within a quadrant. Referring to Fig. 2(a) the modulation

parameter = b/c. For a given transmitted signal power the

sum of b/2 and c should remain constant. The value of

should not exceed the square root of the carrier power pc.

Otherwise, the constellation points of the same quadrant will

overlap. Fig. 2(a), (b) and (c) shows the constellation diagram

for = 3, = 2 and = 1 respectively.

(a) = 3

(b) = 2

(c) = 1

Fig. 2. Constellation diagrams of 16-HQAM

Referring to Fig. 2(c), the minimum distance of all

constellation point is equal (2d=b=c, i.e. HQAM results in

QAM) and the two MSB represent the HP bits which have

lower BER than the two LSB bits. LSB bits are representing

LP bits. It can be seen that the four symbols in every quadrant

have the same HP bits but different LP bits; this is also called

constellation overlapping which ensures the HP bits to be

transmitted correctly [14, 22].

Higher portion of the transmitter power means better

protection against the channel errors than the lower portion of

the power. This is a simple unequal error protection without

introducing any redundancy to the modulated data. In this case

the performance of the HP will be improved at the expense of

LP. However, by increasing the degree of non-uniformity, (b >

c) the improvement of the HP performance is significant, at the

expense of the LP sub-channel.

1001 1011 0001 0011

1000 1010 0000 0010

1101 1111 0101 0111

1101 1111 0100 0110

10

5

-5

-10

Q

-10 -5 5 10

I

1001 1011 0001 0011

1000 1010 0000 0010

1101 1111 0101 0111

1101 1111 0100 0110

Q 10

6

-6

-10

c b = b/c,



V. SIMULATIONS AND RESULTS

A simulation for SPIHT coded image transmission and

reception was carried out using 16-HQAM technique. In this

simulation 512X512 gray-scale Lena image is used as test

image with 9-level wavelet decomposition. Biorthogonal

(bior4.4) wavelet is used as wavelet basis function. The flow

diagram of the proposed image transmission system simulation

is shown in Fig. 3. It has been simulated in MATLAB 7.8

(R2009a). In proposed simulation, source image is

decomposed by Biorthogonal wavelet basis function. SPIHT

encoding technique is applied to the wavelet decomposed

image. After encoding the generated bit stream is partitioned

into two levels based on their sensitivity. Higher sensitive data

bits (e.g. image size, number of bit plane, decomposition level

etc.) are mapped to the HP bits and lower sensitive data bits

are mapped to the LP bits in 16-HQAM modulation

constellation points as shown in Fig. 2(c). After mapping with

the constellation points, the generated signals are transmitted

over wireless erroneous (AWGN and fading) channel. The

received signals with noise are demodulated and merge back to

a single bit stream. SPIHT decoding is applied to the bit

stream. After inverse wavelet decomposition, image data are

written into a graphic file.

Fig. 3. Proposed simulation flow diagram of SPIHT coded image transmission

and reception using 16-HQAM technique

The results have been evaluated by peak-signal-to-noise-

ratio (PSNR) of the received reconstructed image. The PSNR

of an image can be calculated using (1)

MSE

KPSNR 10log10

(1) (1)

y x

yxgyxgNM

MSE2' ,,

1

where, g(x,y) and g(x,y) represent the gray values of the pixels

in the original image and the reconstructed image respectively.

M and N represent the width and height of the image

respectively, while K is the maximal gray value of the image

[15].

This simulation calculates the PSNR value of the received

reconstructed image for different bit-rate (0.1 to 1.0) and also

for different values of modulation parameter (1 to 5) through

AWGN channel and noisy fading channel. PSNR versus Bit-

rate (bpp) curves are shown for the transmission of SPIHT

coded gray-scale image using 16-HQAM technique over

AWGN channel in Fig. 4 and over noisy fading channel in Fig.

5. From Fig. 4, it is shown that for the lower value of , PSNR

vs. bpp curves are not smooth because of channel noise but the

PSNR of the reconstructed image increases when the value of

increases for a fixed bit-rate. Fig. 5 also shows the same

characteristics as Fig. 4 for noisy fading channel.

For the transmission of SPIHT coded images, the SNR was

kept at a fixed value of 18 dB. The results are shown in Fig. 6

for AWGN channel and in Fig. 7 for noisy fading channel

Fig. 4. PSNR vs. Bit-rate graph for different values of modulation parameter

for the transmission over AWGN channel

Fig. 5. PSNR vs. Bit-rate graph for different values of modulation parameter

and transmission over noisy fading channel

HP LP

Transmitted

Image

Read Image data

from a Graphic File

Wavelet

Decomposition

SPIHT

Encoding

Bit Stream

Partitioning

16-HQAM

Modulation

MSBs

LSBs

Signal

Transmission

SPIHT

Decoding

Bit Stream

Merging

16-HQAM

Demodulation

MSBs

LSBs

Signal

Reception

HP LP

Write Image data to

a Graphic File

Inverse Wavelet

Decomposition

Received

Image

Add Noise

with

Transmitted

Signal



where each row represents the reconstructed images for the

fixed value of bit-rate (bpp) and different values of modulation

parameter, and each column represents the reconstructed

images for the fixed value of modulation parameter and

different values of bit-rate. It can be seen that for a fixed value

of bit-rate when the value of increases the quality of the

reconstructed images also improves. For example in Fig. 6,

when bit-rate is 0.2 bpp and =1, there are too much distortion

occurs in the reconstructed image for channels noise.

If increases then the amount of distortion decreases and

gradually increases the quality of the reconstructed images. In

Fig. 7, for a fixed bpp when the value of increases the effects

of fading in the reconstructed images decrease.

VI. CONCLUSIONS

In this paper an image transmission system is proposed

where Hierarchical 16-QAM technique is used to get unequal

error protection for the transmission of SPIHT coded image

over erroneous wireless channels. The advantage of this

system is that unequal error protection is achieved without an

increase in bandwidth in contrast to channel coding that

increases the data rate by adding redundancy to the transmitted

signal. The SPIHT coded image is divided into two sub-

streams (HP and LP) based on their sensitivity and transmits

using 16-HQAM technique through erroneous wireless

channel. Thus the sub-streams obtain unequal error protection

without increasing the bandwidth. Simulation results shows

that the proposed image transmission system can improve

PSNR of the reconstructed images for lower channel SNR

using 16-HQAM technique without any additional hardware.

REFERENCES

[1] Said, A. and Pearlman, W.A., A new, fast, and efficient image codec

based on set partitioning in hierarchical trees, IEEE Trans. on Circuits

and Systems for Video Technology, 6(3), 243250, 1996.

[2] J.M. Shapiro, Embedded image coding using zero-trees of wavelet coefficients, IEEE Transactions Signal Processing, vol. 41, Dec. 1993.

[3] D. Taubman, High Performance scalable image compression with

EBCOT, IEEE Transactions on Image Processing, vol. 9, July 2000.

[4] I. Hontsch and L. Karan Locally adaptive perceptual image coding,

IEEE Transactions on Image Processing, vol. 9, September 2000.

Bit-rate (bpp) =1 =2 =3 =4 =5

0.2

0.4

0.6

0.8

1.0

Fig. 6. Reconstructed images using different bit-rate (bpp) and different value of modulation parameter for AWGN channel



Bit-rate =1 =2 =3 =4 =5

0.2

0.4

0.6

0.8

1.0

Fig. 7. Reconstructed images using different bit-rate (bpp) and different value of modulation parameter for erroneous fading channel

[5] Said, A. and Pearlman,W.A., Image compression using the spatial-

orientation tree, IEEE Int. Symp. on Circuits and Systems, Chicago, IL,

279282, 1993.

[6] Yoong Choon Chang; Sze Wei Lee; Komiya, R.; , "A low-complexity

unequal error protection of H.264/AVC video using adaptive

hierarchical QAM," Consumer Electronics, IEEE Transactions on ,

vol.52, no.4, pp.1153-1158, Nov. 2006.

[7] M. Mahdi Ghandi and M. Ghanbari, "Layered H.264 video transmission

with hierarchical QAM," Elsevier J. Visual Commun. Image

Representation, Special issue on H.264/AVC, vol. 17, no. 2, pp. 451 -

466, April 2006.

[8] B. Barmada, E.V. Jones, Adaptive mapping and priority assignment for

OFDM, Proceedings of the IEE Conference of 3G Mobile

Communication Technologies, London, 2002.

[9] Seamus OLeary, Hierarchical transmission and COFDM systems,

IEEE Transactions on Brod., vol. 43, no. 2, pp. 166-174, June 1997.

[10] Chee-Siong Lee, Thoandmas Keller and Lajos Hanzo, OFDM-Based

turbo-coded hierarchical and non-hierarchical terrestrial mobile digital

video broadcasting, IEEE Transactions on Broadcasting, vol. 46, no.1,

pp. 1-22, March 2000.

[11] B. Barmada, M. M. Ghandi, E. V. Jones, M. Ghanbari, Prioritized

transmission of data partitioned H.264 video with hierarchical QAM,

IEEE Signal Processing Letters, v.12. no.8, pp.577-580, August 2005.

[12] Sadough, S.M.S.; Duhamel, P.; , "On the Interaction Between Channel

Coding and Hierarchical Modulation," Communications, 2009. ICC '09.

IEEE International Conference on , vol., no., pp.1-5, 14-18 June 2009.

[13] W.K.Pratt, Digital Image Processing (New York: Wiley 1978).

[14] L. Hanzo, W. Webb, T. Keller, Single and multi carrier QAM: principles

and applications for personal communications, WLANs and

broadcasting, John Wiley and Sons, 2000.

[15] Yong Liu; Ai-dong Men; Zi-yi Quan; Bo Yang; , "Unequal Error

Protection for SPIHT-Coded Image Transmission Based on MIMO

Adaptive Channel Assignment Policy," Advanced Computer Control,

2009. ICACC '09. vol., no., pp.781-785, 22-24 Jan. 2009.

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ATFECM-30109054