Page 207 - Video Coding for Mobile Communications Efficiency, Complexity, and Resilience

184 Chapter 8. The Simplex Minimization Search

8.5.1.1 Choice of Coe3cients
Before evaluating the performance of the SMS algorithm, suitable values for
the reCection, , contraction, , and expansion, , coeDcients need to be cho-
sen. Figures 8.3, 8.4, and 8.5 show the performance of the SMS algorithm
with di+erent values of ; , and , respectively. The :gures indicate that
the performance of the SMS algorithm is not very sensitive to the choice of
these coeDcients. This may be due to the good performance of the initial-
ization procedure and termination criterion. In general, however, the values
1
=1, = , and = 2 provide the best compromise between computational
2
complexity and prediction quality. In addition, this particular set of coeDcients
reduces the complexity of the SMtransformation equations, Equations (8:4),

QSIF Foreman @ 25 f.p.s., Expansion=2.0, Contraction=0.5 QSIF Foreman @ 25 f.p.s., Expansion=2.0, Contraction=0.5
32.1 1100
1090
32.05 1080
1070
PSNR Y (dB) 32 Searched locations/frame 1060
1050
31.95 1040
1030
31.9 1020
1010
31.85 1000
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
Reflection coefficient, α Reflection coefficient, α
(a) Prediction quality (b) Computational complexity
Figure 8.3: Performance of SMS with di+erent values of the reCection coeDcient

QSIF Foreman @ 25 f.p.s., Reflection=1.0, Expansion=2.0 QSIF Foreman @ 25 f.p.s., Reflection=1.0, Expansion=2.0
32.04 1120
1110
32.03
1100
32.02 1090
PSNR (dB) 32.01 Searched locations/frame 1080
Y
1070
32
31.99 1060
1050
31.98
1040
31.97 1030
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Contraction coefficient, β Contraction coefficient, β
(a) Prediction quality (b) Computational complexity
Figure 8.4: Performance of SMS with di+erent values of the contraction coeDcient