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Table 2: The results of reference methods and proposed algorithm in terms of time per frame and PSNR.
FS NPDS APDS SRAMPD Proposed
Sequences Time PSNR Time PSNR Time PSNR Time PSNR Time PSNR
Akiyo 0.401 42.34 0.039 42.23 0.019 42.32 0.012 42.32 0.0030 42.32
Coastguard 0.413 30.43 0.045 30.32 0.029 30.44 0.013 30.42 0.0053 30.43
Container 0.438 38.17 0.044 38.15 0.026 38.16 0.012 38.03 0.0038 37.99
Flower 0.452 25.89 0.046 25.82 0.033 25.90 0.014 25.85 0.0076 25.87
Foreman 0.407 31.81 0.044 31.69 0.036 31.82 0.016 31.88 0.0062 31.84
Mobile 0.467 25.04 0.046 24.84 0.028 25.02 0.018 24.97 0.0071 25.01
Stefan 0.419 23.90 0.048 23.73 0.049 23.89 0.017 24.71 0.0064 25.00
Mother &daughter 0.415 40.05 0.044 39.94 0.036 40.00 0.012 39.75 0.0047 39.87
Table 0.463 31.46 0.045 31.23 0.029 31.42 0.012 31.47 0.0042 31.27
average 0.430 32.12 0.045 31.99 0.032 32.11 0.014 32.16 0.0054 32.18
Speed-up ratio 1.000 9.659 13.590 30.764 80.190
PSNR difference 0.000 -0.127 -0.014 0.035 0.057
3.2 Search Range Adjustment
Upper
Block
Left
Block
Starting
Point
Right
Block
Bottom
Block
Figure 2: The location of reference blocks.
In SRAMPD, the author refers to left, upper and
upper-left block of initial point for the search range
adjustment. However, in reference frame, there also
exist right and bottom block, and it is more
correlated with initial point than upper-left position
since, in video sequence, the dominant edges are
horizontal and vertical direction. Therefore, in
proposed algorithm, we refer to left, upper, right and
bottom block’s distortion value for the search range
control. The modified collection of SADs follows,
'[ ( ) ].
lurb
C SAD SAD SAD SAD SAD= mv
(4)
The equation (2) is used in proposed method to
obtain a search range; however, we changed the
threshold value experimentally that is much larger
than SRAMPD about 4 times and we utilized
average of C’ instead of maximum SAD because the
proposed method of motion vector prediction is
more accurate than conventional algorithm. Also,
4 EXPERIMENTAL RESULTS
To evaluate performance of the proposed algorithms,
we compare them with FS, NPDS, APDS and
SRAMPD in terms of time per frame and PSNR. We
use speed measurement as time per frame because
each sequences have the different number of frames.
The experimental setup is as follows: the block size
of 16 x 16, the search window size of ±16. Eight
CIF (352 x 288 pixels) video sequences, “Akiyo”,
“Coastguard”, “Container”, “Flower”, “Mobile”,
“Stefan”, “Mother & daughter” and “Table”, are
used.
Table 2 shows the performance of the proposed
algorithms. The proposed algorithms are about 2.6
times faster than SRAMPD, averagely. Especially,
in “Flower” sequence, 5.31 times faster than
conventional method. The proposed method obtains
good performance for almost every sequence even if
the sequence has the fast motion characteristic. In
“Table” sequence, we cannot get a better result in
terms of PSNR because there is a scene change at
131th frame. When the scene is completely shifts,
the common characteristics include the motion
vector filed is not ordinary. Proposed method use the
co-located motion vector in reference frame, it can
be a weak-point if there is breakpoint caused by
scene change or fast and unpredictable motions.
From Table 3, we can observe the search
window size that is almost 2.5 times smaller than
SRAMPD; therefore, our method for search range
adjustment yields speed-up improvement in terms of
time. Especially, for “Akiyo” and “Container”,
proposed algorithm achieves remarkable results
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