Low Complexity HEVC Encoder for Visual Sensor Networks
Abstract
:1. Introduction
2. Motivations and Statistical Analyses
Sequence | QP | Level 0 | Level 1 | Level 2 | Level 3 |
---|---|---|---|---|---|
FourPeople | 22 | 49.48 | 24.79 | 22.13 | 3.60 |
27 | 66.59 | 16.50 | 15.19 | 1.71 | |
32 | 74.69 | 11.88 | 12.46 | 0.98 | |
37 | 79.57 | 8.78 | 11.15 | 0.50 | |
Johnny | 22 | 51.30 | 26.48 | 19.91 | 2.31 |
27 | 68.30 | 17.63 | 13.26 | 0.81 | |
32 | 76.74 | 11.87 | 10.95 | 0.44 | |
37 | 82.57 | 7.58 | 9.59 | 0.25 | |
Vidyo1 | 22 | 48.94 | 26.55 | 21.07 | 3.44 |
27 | 65.94 | 18.27 | 14.51 | 1.27 | |
32 | 74.66 | 13.12 | 11.66 | 0.55 | |
37 | 80.34 | 9.35 | 10.07 | 0.24 | |
Average | 68.26 | 16.07 | 14.33 | 1.34 |
3. The Proposed Low Complexity HEVC Encoder for VSNs
3.1. The Proposed All-Zero Block-Based Fast CU Depth Decision Method
3.2. Efficient Distortion Estimation Based on Spatial Correlation
3.3. The Overall Algorithm
Algorithm 1 Proposed fast CU size decision method for the low complexity H.265/HEVC encoder. |
Input: CTU size = 64 × 64, the maximum quadtree depth level = 4 |
for Depth level=0 to 3 do |
Encode the current CU with the Merge/Skip mode |
Encode the current CU with the inter-2N×2N mode |
if then |
The predictive distortion of the remaining inter-prediction modes is obtained by Equation (4) and Equation (5) |
else |
The predictive distortion of the remaining inter-prediction modes is achieved by the original motion estimation |
end if |
if then |
The CU size decision process is terminated |
else |
Encode the current CTU with the next quadtree depth level |
end if |
Output: The best CU quadtree depth level |
end for |
Process the next CTU |
4. Experimental Results
Sequence | QP | Choi [11] vs. HM | Kim [20] vs. HM | Pan [12] vs. HM | Proposed vs. HM | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ΔPSNR | ΔBR | ΔT | ΔPSNR | ΔBR | ΔT | ΔPSNR | ΔBR | ΔT | ΔPSNR | ΔBR | ΔT | ||
(dB) | (%) | (%) | (dB) | (%) | (%) | (dB) | (%) | (%) | (dB) | (%) | (%) | ||
FourPeople | 22 | −0.030 | −1.32 | −41.84 | −0.008 | 0.07 | −35.70 | −0.014 | −0.08 | −39.27 | −0.034 | −1.13 | −58.04 |
27 | −0.039 | −1.28 | −55.15 | −0.013 | −0.41 | −45.36 | −0.027 | 0.15 | −52.35 | −0.041 | −0.99 | −71.59 | |
32 | −0.025 | −0.65 | −62.45 | 0.001 | −0.12 | −50.67 | −0.029 | 0.81 | −60.81 | −0.047 | −0.57 | −78.38 | |
37 | −0.029 | −0.62 | −67.37 | −0.012 | 0.04 | −54.20 | −0.019 | −0.38 | −66.34 | −0.063 | 0.06 | −82.46 | |
Average | −0.004 | 0.09 | −56.70 | −0.002 | 0.06 | −46.48 | −0.011 | 0.34 | −54.69 | −0.021 | 0.61 | −72.62 | |
Johnny | 22 | −0.024 | −1.22 | −41.67 | −0.010 | −0.52 | −36.45 | −0.019 | −0.32 | −39.61 | −0.032 | −1.04 | −58.17 |
27 | −0.030 | −1.16 | −56.50 | −0.014 | −0.21 | −46.92 | −0.016 | −0.57 | −56.67 | −0.047 | −1.43 | −74.11 | |
32 | −0.046 | −1.21 | −64.98 | −0.029 | −0.35 | −52.46 | −0.042 | −0.53 | −65.08 | −0.064 | −1.62 | −81.09 | |
37 | −0.039 | −1.30 | −70.17 | −0.026 | −0.43 | −56.22 | −0.021 | −0.38 | −69.12 | −0.075 | −1.26 | −85.26 | |
Average | −0.003 | 0.22 | −58.33 | −0.009 | 0.44 | −48.01 | −0.008 | 0.57 | −57.62 | −0.017 | 0.69 | −74.66 | |
KristenAndSara | 22 | −0.035 | −1.12 | −41.23 | −0.010 | −0.28 | −35.41 | −0.014 | −0.08 | −39.27 | −0.047 | −1.08 | −57.91 |
27 | −0.047 | −1.44 | −54.02 | −0.014 | −0.09 | −44.77 | −0.027 | 0.15 | −52.35 | −0.072 | −2.35 | −71.39 | |
32 | −0.050 | −1.99 | −62.69 | −0.013 | 0.00 | −50.86 | −0.029 | 0.81 | −60.81 | −0.079 | −1.59 | −79.14 | |
37 | −0.053 | −1.27 | −67.84 | −0.012 | 0.19 | −54.51 | −0.019 | −0.38 | −66.34 | −0.089 | −0.84 | −83.58 | |
Average | −0.004 | 0.01 | −56.45 | −0.012 | 0.39 | −46.39 | −0.031 | 1.15 | −54.69 | −0.019 | 0.68 | −73.00 | |
Vidyo1 | 22 | −0.034 | −1.40 | −43.63 | −0.003 | 0.21 | −36.67 | −0.014 | −0.36 | −40.42 | −0.042 | −1.51 | −59.66 |
27 | −0.042 | −1.28 | −55.67 | −0.013 | −0.22 | −45.59 | −0.026 | −0.82 | −52.61 | −0.056 | −0.78 | −72.37 | |
32 | −0.043 | −1.06 | −62.99 | −0.024 | −0.46 | −50.79 | −0.027 | −0.40 | −61.17 | −0.063 | −0.57 | −79.38 | |
37 | −0.036 | −1.40 | −68.12 | −0.022 | −0.76 | −54.25 | −0.023 | 0.12 | −65.18 | −0.070 | −0.64 | −83.88 | |
Average | 0.033 | −0.98 | −57.60 | 0.030 | −0.90 | −46.82 | −0.009 | 0.29 | −54.84 | 0.033 | −1.02 | −73.82 | |
Vidyo3 | 22 | −0.038 | −0.80 | −40.47 | −0.010 | −0.16 | −34.68 | −0.017 | −0.29 | −39.41 | −0.053 | −1.11 | −57.07 |
27 | −0.045 | −0.69 | −51.98 | −0.020 | 0.06 | −43.51 | −0.025 | −0.05 | −51.86 | −0.061 | −0.40 | −69.79 | |
32 | −0.059 | −0.84 | −59.36 | −0.041 | −0.30 | −49.01 | −0.036 | 0.52 | −60.94 | −0.114 | −0.39 | −76.91 | |
37 | −0.071 | −0.75 | −65.03 | −0.017 | 0.69 | −52.55 | −0.034 | 0.39 | −66.03 | −0.112 | 0.16 | −81.96 | |
Average | −0.024 | 0.70 | −54.21 | −0.025 | 0.68 | −44.94 | −0.032 | 1.04 | −54.56 | −0.064 | 1.98 | −71.43 | |
Vidyo4 | 22 | −0.029 | −0.69 | −32.54 | −0.008 | −0.09 | −29.06 | −0.015 | −0.26 | −33.82 | −0.030 | −0.72 | −51.27 |
27 | −0.022 | −0.72 | −46.57 | −0.009 | −0.20 | −39.92 | −0.024 | −0.14 | −47.37 | −0.037 | −0.58 | −57.46 | |
32 | −0.026 | −0.38 | −56.17 | −0.011 | 0.29 | −46.56 | −0.016 | −0.03 | −55.37 | −0.041 | 0.70 | −74.86 | |
37 | −0.028 | −0.40 | −62.89 | −0.009 | 0.03 | −50.81 | −0.017 | 0.03 | −60.62 | −0.049 | 0.46 | −80.07 | |
Average | −0.009 | 0.39 | −49.54 | −0.008 | 0.38 | −41.59 | −0.018 | 0.60 | −49.29 | −0.036 | 1.46 | −65.91 | |
Average | −0.002 | 0.07 | −55.47 | −0.005 | 0.18 | −45.71 | −0.018 | 0.67 | −54.28 | −0.021 | 0.73 | −71.91 |
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Pan, Z.; Chen, L.; Sun, X. Low Complexity HEVC Encoder for Visual Sensor Networks. Sensors 2015, 15, 30115-30125. https://doi.org/10.3390/s151229788
Pan Z, Chen L, Sun X. Low Complexity HEVC Encoder for Visual Sensor Networks. Sensors. 2015; 15(12):30115-30125. https://doi.org/10.3390/s151229788
Chicago/Turabian StylePan, Zhaoqing, Liming Chen, and Xingming Sun. 2015. "Low Complexity HEVC Encoder for Visual Sensor Networks" Sensors 15, no. 12: 30115-30125. https://doi.org/10.3390/s151229788
APA StylePan, Z., Chen, L., & Sun, X. (2015). Low Complexity HEVC Encoder for Visual Sensor Networks. Sensors, 15(12), 30115-30125. https://doi.org/10.3390/s151229788