It needs to be noted that for the three images, boats, bridge and harbour, the algorithm comes very close to the established bound with very small errors. It should be recalled that the Slepian-Wolf bound is stated for highly correlated source pairs; and it is obvious that differences greater than +/- 64 in a range of 256 values clearly violate the high correlation (or linear relationship) rule. Hence, for these three images, the algorithm works very efficiently. On the other hand, in airfield and peppers, which are images with many sharp gray level transitions, the algorithm is less successful in reaching the established bound. However, it should be noted the algorithm still does very well compared to the individual coding of these two images, which is the other criterion to analyze the efficiency of the algorithm.
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Table-1) The comparison of the individual (only X), joint (X and Y available
both at the encoder and the decoder) and the distributed coding bit-rates
and the distributed coding error, with D=128 and N=256
 
Note the very low error and the high
proximity of distributed coding bit rate to joint coding bit rate.
 
Note that the H(X) with distributed source coding values for airfield and
pepper are not as close to the joint entropy rate; however, they are still
very much below the individual entropy values.
.
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Table-2) Comparison of the H(X) values achieved for different N values at
D=128.
 
H((X)) values are the entropy values that the algorithm achieves
 
Note that the H values decrease as N increases.
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Table-3a) D=16
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Table-3b) D=32
Table-3) The comparison of the Slepian-Wolf bound (H(X,Y)-H(Y)) to the
H values achieved by the algorithm for D=16 in Table-3a) and D=32
in Table-3b)
 
Note that for relatively small values of D, the algorithm does as good as
the Slepian-Wolf bound very easily
ABSTRACT
INTRODUCTION
PROBLEM DESCRIPTION and PRIOR WORK
DATA SET
ALGORITHM
RESULTS
CONCLUSIONS
REFERENCES