Download - S.P.K. Chaitanya.pdf

7/28/2019 S.P.K. Chaitanya.pdf

1/5

InternatIonal Journalof electronIcs & communIcatIontechnology 213

IJECT Vol. 2, SP-1, DEC. 2011ISSN : 2230-7109(Online) | ISSN : 2230-9543(Print)

w w w . i j e c t . o r g

Abstract

This paper explores the application of the error resilient coding

scheme, absolutely addressed Picture Element coding (APEL),

to image transmission over noisy radio channels. To improve

the performance of APEL at low signal-to-noise ratios, turbo

coding is introduced into the system. Demonstrated through

Gaussian and Rayleigh fading channel simulations, this novel

combination will be shown to correct and restrict the propagation

of the majority of errors incurred during transmission.

I. APEL Image Coding

APEL [1,2] is a loss-less, robust image coding system whichtranslates variable sized pixel areas of pre-dened dimensions

into independent picture blocks (pels). Each pel is issued with

two co-ordinates, x and y, establishing an absolute location with

respect to an origin. As the underlying APEL coding technique

operates on a binary level, the encoding of grey-scale or colour

images employs a Bit Plane Coding (BPC) [3] stage. The BPC

stage furnishes the APEL encoder with a colour coding sequence

to represent a given source image in binary planes.

Taking each extrapolated binary plane in turn, a recognition

algorithm searches through each image looking for square

areas of black pixels; starting with large square pels during

the rst scan, then repeating this process in multiple passesselecting pels of decreasing magnitude. The maximum size

of the initial pel is limited according to the anticipated nature

of the channel, consequently less information is lost should

corruption occur. Once all of the square pels of an efcient

size have been removed from the plane, run-lengths of various

geometries are used to encode the residue. Fig. 1 illustrates

an APEL encoded section of a grey-scale image. Here, it can

be seen how (x,y) co-ordinates are assigned to pels of various

geometries.

The data-stream created from this process can be pictured as

a succession of (x,y) addresses, grouped according to the samesize they represent and interspersed with control symbols (Fig.

2). These symbols not only serve to provide synchronization

markers, but in addition convey pel geometry metrics to the

decoder.

The APEL absolute addressing scheme alleviates the need

for End Of Line (EOL) symbols and, as each codeword isindependent, offers a solution to the problems of horizontal

and vertical error propagation. Additionally, as each pel has

its own address, it is possible to interleave them within the

transmitted data-stream. This versatility can be utilized in

many ways, for example: pels pertaining to important image

detail can either be dispersed throughout the data-stream or

transmitted at the start depending on channel conditions or

operator preference.

II. Application of Turbo Coding

By employing iterative soft decoding principles, turbo codes

[4,5] achieve bit error rates close to the Shannon limit. Thispowerful forward error correction technique has been applied to

APEL coding to provide a robust image communication means

(Fig. 3).

In this study, a rate recursive systematic convolutional code

with constraint length 3 is used as the component of a turbocoding scheme. The encoder employs parallel concatenation

as illustrated in Fig. 4. The binary information to be encoded is

represented as uk, with ck,1 and ck,2 signifying the parity bits

of the rst and second component encoders, and where D

stands for a bit delay. Symbol represents pseudo-random

interleaver, which accepts blocks of 8000 information bits.

Compressed Image Transmission at Low Signal toNoise Ratio for Turbo Code Application

1P.Surendra Kumar, 2P.Ranjith Kumar, 3S.P.Krishna Chaitanya1Dept. of ECE, Bapatla Engineering College, Bapatla, Guntur (Dist.), A.P., India

2Dept. of ECE, Chintalapudi Engineering College, Chintalapudi, Guntur (Dist.), A.P., India3Dept. of ECE, Chaitanya Engineering College, Kommadi, Visakhapatnam, A.P., I ndia


2/5

214 InternatIonal Journalof electronIcs & communIcatIon technology

IJECT Vol. 2, SP-1, DEC. 2011 ISSN : 2230-7109(Online) | ISSN : 2230-9543(Print)


Binary information, uk, is fed to the component encoders in

order to generate the associated parity bits, ck,1 and ck,2,

which are selected alternately by the puncturing block. In

other words, for a binary input sequence of u1, u2, u3, u4,

the encoded sequence would be u1, c1,1, u2, c2,2, u3, c3,1,

u4, c4,2. The fact that the input sequence uk is retained at the

output, is due to the systematic nature of the encoder.

Fig. 5 shows the turbo decoder which contains two componentdecoders concatenated in parallel, performing the sub-optimal

log-MAP algorithm. Lek,i is the extrinsic information related to

the kth information symbol, rk, provided by the ith decoder,

and yk,i is the parity symbol associated with rk, and fed into

the ith component decoder.

In order to perform the MAP algorithm, an accurate estimation

of the channel conditions, i.e. the noise variance, is required.

Hence, a channel reliability factor, Lc, is fed into the decoder,

which equates to 4.a.Eb/No [4]. In this expression, a denotes

the fading amplitude, which is 1 for a Gaussian channel and

Eb/No is the bit energy to single sided noise spectral densityratio.

De-interleaving, -1, is the inverse operation of interleaving and

is performed at the output of the second component decoder.

When the required number of iterations (xed at 16 in our case),

have been completed, the soft output of the second decoder is

de-interleaved and a hard decision is made to obtain rk.

III. Results

Simulations over AWGN and Rayleigh channels, at various

signal-to-noise ratios, verify the excellent performance of this

novel APEL-turbo scheme. Performance comparisons were

carried out between turbo coded APEL, JPEG, and BMP imageles.

To provide a fair comparison between the APEL and JPEG

images, compression level had to be the same. This resulted

in the data reduction for both APEL and JPEG to be around 5

to 1.

In the assessment of bit errors within a received image le,

a quantitative pixel error rate offers a fairer comparison.

Therefore, it is appropriate to represent the error performance

in a ratio called Pixel Error Rate (PER), which is a measure

of the degree of image degradation.

Through the analysis of the ith received pixels variance from its

transmitted value, a measure of visual disturbance, i, can be

quantied as in (1), where ti and ri represent the transmitted and

received pixel colours respectively, for an n colour image.

From (1) it follows that the PER is calculated as in (2)

where X and Y are the horizontal and the

vertical resolution of the image, respectively.

3.1. Gaussian Channel results

The 3 plots shown in Fig. 6 illustrate the average results attained

from 50 tests performed over a simulated AWGN channel.

The number of pixel errors encountered is highlighted from

10 million pixels.

As the gure indicates, the performance of the JPEG-turbo

scheme is very poor in the 1.0 1.4 dB range. This is due to

the inherent fragility of the JPEG structure and its inability to

correct, or restrict the propagation of, any errors. This result

was not unexpected. The variable length Huffman code words

employed by JPEG do not provide any protection against errors

and a single error can lead to catastrophic collapse. The

complete opposite of this interdependence can be seen in

the BMP le format, and in accordance the performance of the

BMP-turbo scheme is good throughout the range. Here, when

errors cannot be repaired by the turbo decoder, only pixels

with corrupted bits are affected. Finally, performance close

to, and at times surpassing, that of the BMP-turbo model is

achieved by the turbo coded APEL. In the region after 1.175

dB, the post-processing techniques employed by APEL recover

many of the damaged pixels which had simply been insertedin the case of BMP.

To observe the visual impact of the pixel errors, samples of the

various le formats have been decoded at a signal-to-noise

of 1.175 dB (Fig. 7). In this example, the incongruous and

clustered nature of the pixel errors in the APEL le, makes it

appear to be of similar quality as the BMP one. However, it

has to be stressed that the APEL le is a fth the size of the

BMP one.

3.2. Rayleigh Channel results

Turbo coded JPEG, bitmap and APEL images were also

transmitted over a Rayleigh channel with a maximum of 200burst errors introduced randomly in each transmitted block.

Fig. 8 illustrates the system performance in the presence of

burst errors.

Due to the severe effects of burst errors, Turbo-JPEG fails to


3/5




maintain data integrity and synchronization after decoding

(Fig. 8A). Again, the fragile structure of the Huffman code

words makes it almost impossible to withstand such channel

conditions. In addition, given that the turbo decoder used in this

application is unable to correct burst errors, image transmission

with Turbo-JPEG becomes very unreliable in such conditions.

Fig. 7: Gaussian channel Results for Eb/No = 1.175 dBA- Turbo coded JPEG, B- Turbo coded APEL, C- Turbo coded

BMP

Turbo-APEL (Fig. 8B) performance in the presence of burst

errors, is again visually comparable to that of Turbo-BMP.

Channel errors which affect pels from various bit planes can

be corrected through an analysis of the other planes. In other

words, the post-processing techniques introduced by APEL

coding provide a powerful means of interpolating pixels using

valid image information. However, as the number of burst errors

per information block is increased, distortion in APEL images

remains noticeable despite the post-processing.


4/5

216 InternatIonal Journalof electronIcs & communIcatIon technology

IJECT Vol. 2, SP-1, DEC. 2011 ISSN : 2230-7109(Online) | ISSN : 2230-9543(Print)


Fig. 8: Rayleigh fading channel results for Eb/No = 4.0 dBA- Turbo coded JPEG, B- Turbo coded APEL, C- Turbo coded

BMP

Unlike the Gaussian channel, the PER plots produced by the

more severe Rayleigh case were found to be inconsistent and

vary from one transmission to the next. It was also observed that

the dynamic range of the decoded image quality was noticeable.

Hence the use of a different measure for evaluating the quality

of the decoded images has been investigated. When evaluating

image performance, the most appropriate tool to employ is the

human eye, as after all the recipient of the transmission will

be human. Keeping this point in mind, a psychometric study

based on the subjective rating of a group of 20 individuals from

various backgrounds, was conducted to obtain mathematical

data for quantifying pixel disturbance levels.

Each individual was shown typical turbo coded APEL and a

BMP image les transmitted over the Rayleigh fading channel,

and then asked to rate each of them in a scale from 1 to 10

(a higher rating indicates better quality). This procedure was

repeated for various channel conditions to generate a range

of sample points (see Fig. 9).

In the opinion of the test subjects, the BMP images that appear

in Fig. 9 are clearer than the APEL ones below 3.7 dB. After

this point, APEL image quality is perceivably better than the

BMP one by a signicant amount. The small perturbations in

the curves correlate to specic images where sensitive areasof a particular image are damaged. When burst errors are

concentrated on image detail which is deemed more important,

e.g. the cats face, they tend to disturb the human observer

more [6]. Consequently, in some cases the perceptual quality

of an image can locally decrease despite improved channel

conditions. This is exemplied at 5 dB where several APEL

errors obscured an area of the cats face. In the case of BMP

images, since the pixel errors appear as trails of corruption,

the perceptual quality increases more gradually as the channel

improves.

Fig. 9: Psychometric evaluation of turbo coded BMP and APELimages simulated over Rayleigh Fading Channel

IV. Conclusion

We have proposed the combination of APEL and turbo coding to

provide a reliable, compressed image transmission system at

low signal-to-noise ratios. Even though APEL coding is used with

a turbo decoder that is not powerful enough to correct heavy

burst errors, the interleaving stage within the APEL structure

is observed to minimize the visual impact of errors. This visual

improvement is achieved through the dissemination of burst

errors both the constituent bit-planes and the entire image,

thus providing an interleaver gain at the decoder.Whilst the majority of bit errors within the APEL image are

corrected via iterative decoding, any which fail to be detected

(and thus perhaps be falsely inserted as erroneous pixels) are

restricted as a result of the robust data structure. Since the


5/5




interleaving stage in APEL coding distributes pixel errors across

the entire image (Fig. 8B), the resulting small clustered errors

that occur in this case can often be less disturbing to the eye

than erroneous pixel trails (Fig. 8C), as Fig. 9 suggests.

The novel combination of the APEL source/channel image

coding technique and additional channel protection provides

not only a resilience to Gaussian type errors, but also offers a

powerful tool for the restriction of burst errors.

References

[1] Chippendale, P., Honary, B., Arthur, P., Maundrell, M.:

International Patent Ref.: PCT GB 98/01877, Data

Encoding System

[2] Chippendale, P.: Transmission of images over time-varying

channels, PhD Thesis, August 1998

[3] McConnell, Bodson, Schaphorst, 1992, FAX : Facsimile

Technology and Applications Handbook, ISBN 0 89006

495 5

[4] Berrou, C., Glavieux, A., Thitimajshima, P.: Near Shannon

Limit Error Correcting Coding and Decoding: Turbo-Codes,

IEEE Proc. ICC 93 Geneva, Switzerland, May 1993, pp.1064-1070

[5] Hagenauer, J., Offer, E., Papke, L.: Iterative Decoding of

Binary Block and Convolutional Codes, IEEE Transactions

on Information Theory, Vol. 42, No. 2, March 1996

[6] Kosslyn M.S., Osherson D.N., Visual Object Recognition,

Irving Biederman, Visual Cognition, 1995 Massachusetts

Institute of Technology, volume 2, pp. 121-165 Bio Data

of Author(S)

P.Surendra Kumar, received his M.Tech from

NIT Sutathkal Karnataka in Department of

Electronics and Communication Engineering,

Presently doing Ph.d in Turbo Coding at

Nagarjuna University, Guntur. He is having

six years of teaching experience as a lecturer

in Bapatla Engineering College, Bapatla. His

areas of interests are Turbo Coding, Digital

Image processing, Electromagnetics and

Antennas

P.Ranjith Kumar, received his M.Tech

from Chaitanya Engineering College,

Visakhapatnam. Currently, he is working

as a Assistant Professor in the Departmentof ECE, Chintalapudi Engineering College,

Chintalapudi in Guntur (Dist), and A.P. His

area of interest is Digital Image processing.

Radar Signal Processing, Antennas.

S.P.Krishna Chaitanya, received his

M.Tech from Andhra University College

of Engineering. Currently, he is working

as Assistant Professor in the Department

of ECE, Chaitanya Engineering College.

His areas of interests are Radar signalProcessing, Electromagnetics, Radar

Cross-Section studies, Antennas and Image

Processing.