Abstract
This paper proposes an ensemble of multi-layer perceptron (MLP) networks for side information (SI) generation in distributed video coding (DVC). In the proposed scheme, both three-layer and four-layer MLP structures are used to form the ensemble model. The proposed model includes four sub-modules. The first sub-module involves the training of the individual networks. The second sub-module selects ‘M’ number of trained MLPs based on the mean square error (MSE) performance metric. Next, the third sub-module involves the testing phase of each of the selected MLPs. Finally, in the last sub-module, the overall ensemble SI is generated using a dynamically averaging (DA) method. The primary goal of this work is to minimize the estimation error between the SI and the corresponding Wyner-Ziv (WZ) frame so that the overall efficiency of DVC codec can be increased. The proposed scheme is evaluated with respect to different parameters such as Rate-Distortion (RD), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and number of parity requests made per estimated frame. The evaluation indicates that the proposed ensemble model shows better generalization capabilities with improved PSNR (in dB) as compared to each of the individual selected networks. Additionally, the comparative analysis also exhibits that the proposed SI generation scheme generates better SI frames in comparison with the contemporary techniques. Further, using a statistical test, namely, ANOVA with significance level of 5%, it has been validated that the proposed technique yields a significant enhancement in the performance as compared to that of the benchmark schemes.
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Aaron A, Zhang R, Girod B (2002) Wyner-Ziv coding of motion video. In: 2002 IEEE Conference record of the thirty-sixth asilomar conference on signals, systems and computers, vol 1, pp 240–244
Aaron A, Setton E, Girod B (2003) Towards practical Wyner-Ziv coding of video. In: IEEE International conference on image processing, ICIP 2003, Proceedings, vol 3, pp III–869
Aaron A, Rane SD, Setton E, Girod B (2004) Transform-domain Wyner-Ziv codec for video. In: Electronic imaging 2004. International Society for Optics and Photonics, pp 520–528
Adhikari R (2015) A neural network based linear ensemble framework for time series forecasting. Neurocomputing 157:231–242
Adikari ABB, Fernando WAC, Arachchi HK, Weerakkody WARJ (2006) Multiple side information streams for distributed video coding. Electron Lett 42(25):1447–1449
Artigas X, Ascenso J, Dalai M, Klomp S, Kubasov D, Ouaret M (2007) The DISCOVER codec: architecture, techniques and evaluation. In: Picture coding symposium (PCS”07) (No. MMSPL- CONF-2009-014). Lisbon
Ascenso J, Brites C, Pereira F (2005) Improving frame interpolation with spatial motion smoothing for pixel domain distributed video coding. In: 5th EURASIP conference on speech and image processing, multimedia communications and services. Smolenice, pp 1–6
Ascenso J, Brites C, Pereira F (2010) A flexible side information generation framework for distributed video coding. Multimed Tools Appl 48(3):381–409
Bhandari S, Patel N (2017) Nonlinear adaptive control of a fixed-wing UAV using multilayer perceptrons. In: AIAA Guidance, navigation, and control conference, pp 1524
Brites C (2005) Advances on distributed video coding. Instituto Superior Técnico, MS Thesis
Brites C, Pereira F (2008) Correlation noise modeling for efficient pixel and transform domain Wyner–Ziv video coding. IEEE Trans Circ Syst Vid Technol 18 (9):1177–1190
Brites C, Ascenso J, Pedro JQ, Pereira F (2008) Evaluating a feedback channel based transform domain Wyner–Ziv video codec. Signal Process Image Commun 23 (4):269–297
Cao MS, Pan LX, Gao YF, Novák D, Ding ZC, Lehký D, Li XL (2015) Neural network ensemble-based parameter sensitivity analysis in civil engineering systems. Neural Comput Applic 1–8
Cheng MH, Leou JJ (2008) A new side information generation scheme for distributed video coding. Adv Multimed Inf Process-PCM 2008:782–785
Choi BD, Han JW, Kim CS, Ko SJ (2007) Motion-compensated frame interpolation using bilateral motion estimation and adaptive overlapped block motion compensation. IEEE Trans Circ Syst Vid Technol 17(4):407–416
DISCOVER-Distributed Coding for Video Services (2005). [Online]. Available: http://www.discoverdvc.org/. Accessed 29 Jul (2009)
Girod B, Aaron AM, Rane S, Rebollo-Monedero D (2005) Distributed video coding. Proc IEEE Special Issue Adv Vid Cod Deliv 93(1):71–83
Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 12(10):993–1001
Hänsel R, Müller E (2011) Global motion guided adaptive temporal inter-/extrapolation for side information generation in distributed video coding. In: 18th IEEE International conference on image processing (ICIP), pp 2629–2632
Hameed AA, Karlik B, Salman MS (2016) Back-propagation algorithm with variable adaptive momentum. Knowl-Based Syst 114:79–87
Hossain MS, Ong ZC, Ng SC, Ismail Z, Khoo SY (2017) Inverse identification of impact locations using multilayer perceptron with effective time-domain feature. Invers Probl Sci Eng 1–19
Islam MF, Kamruzzaman J (2006) ANN ensemble and output encoding scheme for improved transformer tap- changer operation. In: Power systems conference and exposition, PSCE’06. IEEE PES, pp 1063–1068
Jiménez D (1998) Dynamically weighted ensemble neural networks for classification. In: 1998 IEEE World Congress on computational intelligence. The 1998 IEEE international joint conference on neural networks proceedings vol 1, pp 753–756
Ko B, Shim H, Jeon B (2007) Wyner-Ziv video coding with side matching for improved side information. Adv Image Vid Technol 816–825
Krogh A, Vedelsby J (1995) Neural network ensembles, cross validation, and active learning. Adv Neural Inf Process Syst 7:231–238
Kubasov D, Nayak J, Guillemot C (2007) Optimal reconstruction in Wyner-Ziv video coding with multiple side information. In: IEEE 9th Workshop on multimedia signal processing, MMSP 2007, pp 183–186
Liu Y, Yao X (1999) Ensemble learning via negative correlation. Neural Netw 12(10):1399–1404
Maqsood I, Khan MR, Abraham A (2004) An ensemble of neural networks for weather forecasting. Neural Comput Appl 13(2):112–122
Moretti F, Pizzuti S, Panzieri S, Annunziato M (2015) Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling. Neurocomputing 167:3–7
Mukherjee D, Macchiavello B, de Queiroz RL (2007) A simple reversed-complexity Wyner-Ziv video coding mode based on a spatial reduction framework. In: Electronic imaging 2007. International Society for Optics and Photonics, pp 65081Y–65081Y
Neelakanta PS, DeGroff D (1994) Neural network modeling: statistical mechanics and cybernetic perspectives. CRC Press, Boca Raton
Opitz DW, Shavlik JW (1996) Actively searching for an effective neural network ensemble. Connect Sci 8(3-4):337–354
Optiz DW, Shavlik JW (1996) Generating accurate and diverse members of a neural-network ensemble. Adv Neural Inf Process Syst. 535–541
Puri R, Ramchandran K (2002) PRISM: A new robust video coding architecture based on distributed compression principles. In: Proceedings of the annual allerton conference on communication control and computing, vol 40, No 1. The University, pp 586–595, 1998
Puri R, Majumdar A, Ramchandran K (2007) PRISM: a video coding paradigm with motion estimation at the decoder. IEEE Trans Image Process 16(10):2436–2448
Rencher AC, Trenkler G (1996) Methods of multivariate analysis. Comput Stat Data Anal 22(3):334
Rup S, Majhi B, Padhy S (2014) An improved side information generation for distributed video coding. AEU-Int J Electron Commun 68(3):201–209
Slepian D, Wolf J (1973) Noiseless coding of correlated information sources. IEEE Trans Inf Theory 19(4):471–480
Standard Video Sequences (2017). https://media.xiph.org/video/derf
Tagliasacchi M, Tubaro S, Sarti A (2006) On the modeling of motion in Wyner-Ziv video coding. In: 2006 IEEE International conference on image processing, pp 593-596
Wyner A, Ziv J (1976) The rate-distortion function for source coding with side information at the decoder. IEEE Trans Inf Theory 22(1):1–10
Yang WA, Zhou W (2015) Autoregressive coefficient-invariant control chart pattern recognition in autocorrelated manufacturing processes using neural network ensemble. J Intell Manuf 26(6):1161–1180
Ye S, Ouaret M, Dufaux F, Ebrahimi T (2009) Improved side information generation for distributed video coding by exploiting spatial and temporal correlations. EURASIP J Image Vid Process 2009(1):683510
Zhou ZH, Chen SF (2002) Neural network ensemble. Chin J Comput-Chin Edn 25(1):1–8
Zhou ZH, Wu J, Tang W (2002) Ensembling neural networks: many could be better than all. Artif Intell 137(1–2):239–263
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Dash, B., Rup, S., Mohapatra, A. et al. Decoder driven side information generation using ensemble of MLP networks for distributed video coding. Multimed Tools Appl 77, 15221–15250 (2018). https://doi.org/10.1007/s11042-017-5103-1
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DOI: https://doi.org/10.1007/s11042-017-5103-1