4.4 Simulation Results
4.4.2 Complexity Comparison
The influence of Schemes 1 and 2 on the complexity is obvious. Since Scheme 1 proves to be ineffective in performance improvement, only the complexity of Scheme 2 is compared to that of the JM. In all the complexity-rate dia-grams, the complexities of Scheme 2 and the JM change slowly with the rate.
Therefore, the relative complexity reduction calculated from the average com-plexities over all the tested rates is a good measure for the effectiveness of Scheme 2. The reduction values are given in Tab. 4.1.
It can be found in Tab. 4.1 that when the slice loss ratio is high, Scheme 2 is more effective. The reason is that a higher loss ratio generally causes greater distortion for the inter modes. In this case, the probability of early terminations in mode selection is higher.
The relative complexity reduction for the video sequence Mobile is re-markably lower than for the other video sequences. The reason is that Mobile has a very complex spatial texture which results in great rates for the intra modes. Therefore early terminations in mode selection are less probable than for the other video sequences.
38 39 40 41 42 43 44 45
50 100 150 200 250
PSNR-Y (dB)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(a)
0 50 100 150 200
50 100 150 200 250 CPU clock ticks ( × 10
6)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(b)
Figure 4.1: The (a) performance and (b) complexity of the schemes for Akiyo and slice loss ratio p= 5%.
34 35 36 37 38 39 40 41
50 100 150 200 250
PSNR-Y (dB)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(a)
0 50 100 150 200
50 100 150 200 250 CPU clock ticks ( × 10
6)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(b)
Figure 4.2: The (a) performance and (b) complexity of the schemes for Akiyo and slice loss ratiop= 20%.
33 34 35 36 37 38 39 40 41 42
50 100 150 200 250
PSNR-Y (dB)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(a)
0 50 100 150 200 250
50 100 150 200 250 CPU clock ticks ( × 10
6)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(b)
Figure 4.3: The (a) performance and (b) complexity of the schemes for Mother and Daughter, and slice loss ratio p= 5%.
32 32.5 33 33.5 34 34.5 35 35.5 36 36.5 37
50 100 150 200 250
PSNR-Y (dB)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(a)
0 50 100 150 200 250
50 100 150 200 250 CPU clock ticks ( × 10
6)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(b)
Figure 4.4: The (a) performance and (b) complexity of the schemes for Mother and Daughter, and slice loss ratio p= 20%.
28 29 30 31 32 33 34 35 36
50 100 150 200 250
PSNR-Y (dB)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(a)
0 50 100 150 200 250 300 350 400
50 100 150 200 250 CPU clock ticks ( × 10
6)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(b)
Figure 4.5: The (a) performance and (b) complexity of the schemes for Car-phone and slice loss ratio p= 5%.
25.5 26 26.5 27 27.5 28 28.5 29 29.5 30 30.5
50 100 150 200 250
PSNR-Y (dB)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(a)
0 50 100 150 200 250 300 350 400
50 100 150 200 250 CPU clock ticks ( × 10
6)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(b)
Figure 4.6: The (a) performance and (b) complexity of the schemes for Car-phone and slice loss ratio p= 20%.
25 26 27 28 29 30 31 32
50 100 150 200 250
PSNR-Y (dB)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(a)
0 50 100 150 200 250 300
50 100 150 200 250 CPU clock ticks ( × 10
6)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(b)
Figure 4.7: The (a) performance and (b) complexity of the schemes for Fore-man and slice loss ratio p= 5%.
22.5 23 23.5 24 24.5 25 25.5 26 26.5
50 100 150 200 250
PSNR-Y (dB)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(a)
0 50 100 150 200 250 300
50 100 150 200 250 CPU clock ticks ( × 10
6)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(b)
Figure 4.8: The (a) performance and (b) complexity of the schemes for Fore-man and slice loss ratiop= 20%.
19 20 21 22 23 24 25 26 27
50 100 150 200 250
PSNR-Y (dB)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(a)
0 50 100 150 200 250 300
50 100 150 200 250 CPU clock ticks ( × 10
6)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(b)
Figure 4.9: The (a) performance and (b) complexity of the schemes for Mobile and slice loss ratio p= 5%.
17.5 18 18.5 19 19.5 20 20.5 21 21.5 22 22.5
50 100 150 200 250
PSNR-Y (dB)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(a)
0 50 100 150 200 250 300
50 100 150 200 250 CPU clock ticks ( × 10
6)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(b)
Figure 4.10: The (a) performance and (b) complexity of the schemes for Mobile and slice loss ratiop= 20%.
26 26.5 27 27.5 28 28.5 29 29.5 30 30.5 31 31.5
50 100 150 200 250
PSNR-Y (dB)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(a)
0 50 100 150 200 250 300
50 100 150 200 250 CPU clock ticks ( × 10
6)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(b)
Figure 4.11: The (a) performance and (b) complexity of the schemes for Coastguard and slice loss ratio p= 5%.
23.5 24 24.5 25 25.5 26 26.5 27 27.5
50 100 150 200 250
PSNR-Y (dB)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(a)
0 50 100 150 200 250 300
50 100 150 200 250 CPU clock ticks ( × 10
6)
Bit rate (kbps) JM 10.2 Scheme 1 Scheme 2
(b)
Figure 4.12: The (a) performance and (b) complexity of the schemes for Coastguard and slice loss ratio p= 20%.
Table 4.2: Relative complexity reduction through Scheme 2 compared to JM.
Video Sequence p= 5% p= 10% p= 15% p= 20%
Akiyo 24% 27% 28% 29%
Mother and Daughter 21% 24% 27% 30%
Carphone 21% 25% 30% 33%
Foreman 23% 30% 34% 39%
Mobile 6% 9% 14% 18%
Coastguard 16% 23% 28% 31%
Chapter 5 Conclusion
In this dissertation, the existing H.264 rate-distortion optimization (RDO) and error resilient rate-distortion optimization (ER-RDO) frameworks are investigated and two modified schemes to the ER-RDO framework are pro-posed and tested.
The RDO framework has proven to be efficient in encoder parameter se-lection in hybrid video coding. Generally it achieves a satisfying balance between the computational simplicity and the compression ratio. The analy-sis in this dissertation helps to better understand the source of the efficiency of RDO.
ER-RDO is an extension of RDO which considers error propagation in packet lossy environments, such as communication networks or storage de-vices. It is shown that ER-RDO can be viewed as a special intra refreshing method. Like RDO, it has been successful in macroblock (MB) mode se-lection in error resilient encoding. However, the extension from RDO to ER-RDO is not straightforward. In RDO, the convexity of the operating distortion-rate functions makes the problem suitable to be solved with the Lagrange multipliers method. In packet lossy environments, the operating distortion-functions are no longer convex due to the different rate and distor-tion characteristics of intra and inter modes. For the purpose of simplicity, ER-RDO ignores the non-convex condition and adopts the same optimiza-tion procedures as RDO. This technical handling is a practical soluoptimiza-tion but the performance may be farther from the optimum.
The first proposed modification of the ER-RDO framework called Scheme 1 is testing two more QPs with only intra modes. Ideally, this modification would not increase the complexity too much. At the same time, the search range is broadened and an improvement in performance is expected. How-ever, the simulation results show that the video quality can not be definitely improved by the modification. The reason might be that the existing QP and mode search range is large enough to cover some nearly-optimal solutions.
The second modification referred to as Scheme 2 is a fast mode selec-tion scheme. Since the clusters of the intra operating points and the inter points are generally far from each other, using an inter mode to represent all the inter modes would not significantly affect intra refreshing decision.
This modification uses the P8×8 mode as the representative inter mode in the decision. The simulation results show that the video quality is almost unchanged while the computational burden is substantially lowered.
As a conclusion, the performance of the existing H.264 ER-RDO frame-work can hardly be improved by moderately increasing the complexity, but it can be maintained even with substantially lowered complexity.
Appendix A
Test Video Sequences
Mother and Daughter Akiyo
Carphone Foreman
Mobile Coastguard
Appendix B
A New Formulation of the Intra-refreshing Problem
In Sec. 3.4, it is pointed out that the existing ER-RDO framework is efficient in locating forced intra MBs although its theoretical basis is incomplete. Here a new formulation of the intra-refreshing problem is developed for possible improvement or alternatives to ER-RDO.
B.1 The Original Problem
In a packet lossy environment, the optimal QS and mode Selection including intra-refreshing problem is given by Eq. (3.15), i.e.,
arg min
u K
X
k=1
E(dk(mk, qk)), subject to
K
X
k=1
rk(mk, qk)≤R0.