1. UNIVERSITY OF BRITISH COLUMBIA RESEARCH PROGRESS IN DEC 2014 Bambang A.B. Sarif

2. Summary Problem: Minimizing energy consumption of Video Sensor Network Previous work:  complexity and bitrate model, for different GOP size and motion estimation level, i.e., block size candidates used. Current work  incorporate the effect of QP and spatial information (SI) and temporal information (TI) values into the model  result: Complexity modeling: correlation 0.983, RMSE=786 mil. instruction Bitrate modeling: correlation 0.977, RMSE=46.5 kbps (better than modified ICIP 2014 paper 0.927 and 94.34) Plan  Write a journal paper on the model  Incorporate the model into an optimization process  Write the thesis 2

3. Video Sensor Networks 3 Minimizing energy consumption is very important - Encoding power consumption - Communication (transmission and reception) power consumption  Find the encoding configuration that optimize the energy consumption

4. Our video datasets:  Different event settings: office, classroom, party  Different camera FoV  Motion level varies per each camera and also during each shot (10s of video) 4

5. For each event (office, classroom, and party), we have 4 scenes from 9 cameras. In total, we have 108 videos Each video has different spatial information (SI) and temporal information (TI) (ITU-T Recommendation) 5 non-standard version uses mean value instead of max (ICIP 2011)

6. Complexity and Bitrate model Power-Rate-Distortion model (Zhihai He et. al., IEEE Trans. CSVT 2005 ) Used in simulation of 9 video nodes, where each node is assumed to have the same  2 (Yifeng He et. al., IEEE Trans. CSVT 2009) Ma’s Model (IEEE Trans. CSVT 2012)  Perceptual quality and bitrate model for different QP and frame rate  Features used: frame difference, normalized frame difference, MV, displaced frame difference, motion activity intensity, MV normalized by contrast, MV normalized by intensity, MV normalized by variance 6 encoding power efficiency, given as a parameter in simulation video variance R max, a and b are obtained using least square regression of features

7. Lottermann’s model (ICIP 2014)  Follows Ma’s model, but use non-standard spatial information unit (SI) and temporal information unit (TI)  6 videos for training and 4 videos for test  120 select frames of videos where SI and TI values are stable  QP from 24 until 45 step size 1  Frame rate: 15 fps, 10 fps, 5 fps and 3 fps  R max, a and b are estimated using least square regression with cross validation error from the features, in the form of p 1 *x 1 + p 2 *x 2 +…. + p n *x n, with x i  {TI, SI, log(TI), log(SI), SI*TI, log(SI*TI)} R max = 0.8149 ·TI·SI + 139.4 a = 2.0123 ·log(SI) – 0.0004 ·TI ·SI – 0.4616 b = 0.1334 ·log(SI·TI) – 0.3072 7

8. Our Model  QP is from 28 until 40 with step size of 2  Frame rate is 15 fps, but GOP size varies={1,2,4,8,16,32,64} Note: the increase of complexity (and decrease of bitrate) between GOP size 32 and 64 is very small  Motion estimation level, is defined as follow 8

9. Complexity model Bitrate model  f(GOP) =  2 ·log(GOP)  For f(  ML ), we check three different functions: 9 C I, C P,  1 and  are estimated from the training set using the same features used by the Lottermann model R I, R P,  and parameters for f(  ML ) and f(GOP) are estimated from the training set using the same features used by the Lottermann model The one used in our IARIA paper. However, in that paper, the value of  3 is not derived from SI/TI.

10. For comparison, we modify the Lottermann model to include  ML. Complexity model Bitrate model 10 C I, a, b, and c are estimated from the training set using the same features used by the Lottermann model R I, d, e and f are estimated from the training set using the same features used by the Lottermann model

11. Training: 27 videos (office_1, classroom_1, party_1); test: 81 videos Results compared to modified Lottermann model (ICIP 2014) Noticed few things:  The bitrate estimation error is significantly lower if we use non-standard SI/TI  If we use standard SI/TI, the above result is the best. If we use different training set (i.e., office_4, classroom_3 and party_2), the result is worse or even bad, especially in % of error.  If the non-standard SI/TI is used (ICIP 2011), the result doesn’t change too much, regardless of which training set I use. Note:  The papers in IEEE Trans. CVST and ICIP that I use as reference do not compare % of error. They only provide the PC (Pearson Correlation) coefficient and RMSE. 11

12. Complexity for different ML and GOP size (QP=28), office_2 cam1 video 12 Bitrate for different QP and GOP size, office_2 cam1 video

13. References :  ITU-R, “P.910: Subjective video quality assessment methods for multimedia applications,” Tech. Rep. P.910, ITU-R (1992).  Zhihai He, Yongfang Liang, Lulin Chen, Ishfaq Ahmad, and Dapeng Wu, “Power-Rate-Distortion Analysis for Wireless Video Communication Under Energy Constraints”, IEEE Trans. CSVT, Vol. 15, No. 5, May 2005  Zhihai He, and Dapeng Wu, “Resource Allocation and Performance Analysis of Wireless Video Sensors”, IEEE Trans. CSVT, Vol. 16, No. 5, May 2006  Yifeng He, Ivan Lee, and Ling Guan, “Distributed Algorithms for Network Lifetime Maximization in Wireless Visual Sensor Networks”, IEEE Trans. CSVT, Vol. 19, No. 5, May 2009  Yang Peng and Eckehard Steinbach, A Novel Full-reference Video Quality Metric and its Application to Wireless Video Transmission, ICIP 2011.  Yen-Fu Ou, Zhan Ma, Tao Liu, and Yao Wang, "Perceptual Quality Assessment of Video Considering Both Frame Rate and Quantization Artifacts", IEEE Trans. CSVT, Vol. 21, No. 3, March 2011  Zhan Ma, Meng Xu, Yen-Fu Ou, and Yao Wang, “Modeling of Rate and Perceptual Quality of Compressed Video as Functions of Frame Rate and Quantization Stepsize and Its Applications”, ", IEEE Trans. CSVT, Vol. 22, No. 5, May 2012  Christian Lottermann, Alexander Machado, Damien Schroeder, Yang Peng, and Eckehard Steinbach, “Bit Rate Estimation for H.264/AVC Video Encoding Based on Temporal and Spatial Activities”, ICIP 2014 13