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Efficient High Quality Video Assessment Using Salient Features
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High Definition (HD) devices requires HD-videos for the effective uses of HD devices. However, it consists of some issues such as high storage capacity, limited battery power of high definition devices, long encoding time, and high computational complexity when it comes to the transmission, broadcasting and internet traffic. Many existing techniques consists these above-mentioned issues. Therefore, there is a need of an efficient technique, which reduces unnecessary amount of space, provides high compression rate and requires low bandwidth spectrum. Therefore, in the paper we have introduced an efficient video compression technique as modified HEVC coding based on saliency features to counter these existing drawbacks. We highlight first, on extracting features on the raw data and then compressed it largely. This technique makes our model powerful and provides effective performance in terms of compression. Our experiment results proves that our model provide better efficiency in terms of average PSNR, MSE and bitrate. Our experimental results outperforms all the existing techniques in terms of saliency map detection, AUC, NSS, KLD and JSD. The average AUC, NSS and KLD value by our proposed method are 0.846, 1.702 and 0.532 respectively which is very high compare to other existing technique.
Keywords
HEVC, AUC, NSS, Encoding.
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- Cisco, “Cisco Visual Networking Index: Forecast and Methodology”, Available at: https://www. cisco.
- com/c/en/us/solutions/collateral/service-provider/visualnetworking-index-vni/complete-white-paper-c11-481360.html.
- J. Ostermann et al., “Video Coding with H.264/AVC: Tools, Performance, and Complexity”, IEEE Circuits and Systems Magazine, Vol. 4, No. 4, pp. 7-28, 2004.
- G.J. Sullivan, J.R. Ohm, W. Han and T. Wiegand, “Overview of the High Efficiency Video Coding (HEVC) Standard”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 22, No. 12, pp. 1649-1668, 2012.
- R.C. Reininger and J.D. Gibson, “Distributions of the TwoDimensional DCT Coefficients for Images” , IEEE Transactions on Communications, Vol. 31, No. 6, pp. 835839, 1983.
- S.R. Smooth and R.A. Rowe, “Study of DCT Coefficients Distributions”, Proceedings of International Conference on Human Vision and Electronic Imaging, pp. 365-368, 1996.
- N. Kamaci, Y. Altunbasak and R.M. Merereau, “Frame Bit Allocation for the H.264/AVC Video Coder Via CauchyDensity-based Rate and Distortion Models”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 15, No. 8, pp. 994-1006, 2005.
- Z. He, Y.K. Kim and S.K. Mitra, “Low-Delay Rate Control for DCT Video Coding Via ρ-Domain Source Modeling”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 11, No. 8, pp. 928-940, 2001.
- J. Sun, Y. Duan, J. Li, J. Liu and Z. Guo, “Rate-Distortion Analysis of Dead-Zone Plus Uniform Threshold Scalar Quantization and its Application-Part II: Two-Pass VBR Coding for H.264/AVC”, IEEE Transactions on Image Processing, Vol. 22, No. 1, pp. 215-228, 2013.
- J. Hou, S. Wan, Z. Ma and L.P. Chau, “Consistent Video Quality Control in Scalable Video Coding using Dependent Distortion Quantization Model”, IEEE Transactions on Broadcasting, Vol. 59, No. 4, pp. 717-724, 2013.
- Y.H. Tan, C. Yeo and Z. Li, “Single-Pass Rate Control with Texture and Non-Texture Rate-Distortion Models”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 22, No. 8, pp. 1236-1245, 2012.
- C.Y. Wu and P.C. Su, “A Content-Adaptive Distortion– Quantization Model for H.264AVC and its Applications”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 24, No. 1, pp. 113-126, 2014.
- M. Tagliasacchi, G. Valenzise and S. Tubaro, “Minimum Variance Optimal Rate Allocation for Multiplexed H.264/AVC Streams”, IEEE Transactions on Image Processing, Vol. 17, No. 7, pp. 1129-1143, 2008.
- Myunghoon Jeon, Namgi Kim and Byoung-Dai Lee, “MapReduce-based Distributed Video Encoding using Content-Aware Video Segmentation and Scheduling”, IEEE Access, Vol. 4, pp. 6802-6815, 2016.
- A. Ilic, S. Momcilovic, N. Roma and L. Sousa, “Adaptive Scheduling Framework for Real-Time Video Encoding on Heterogeneous Systems”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 26, No. 3, pp. 597611, 2016.
- K.L. Chung, Y.H. Huang, C.H. Lin and J.P. Fang, “Novel Bitrate Saving and Fast Coding for Depth Videos in 3DHEVC”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 26, No. 10, pp. 1859-1869, 2016.
- G. Kim, K. Yi and C.M. Kyung, “A Content-Aware Video Encoding Scheme Based on Single-Pass Consistent Quality Control”, IEEE Transactions on Broadcasting, Vol. 62, No.4, pp. 800-816, 2016.
- J. Chao and E. Steinbach, “Keypoint Encoding for Improved Feature Extraction From Compressed Video at Low Bitrates”, IEEE Transactions on Multimedia, Vol. 18, No. 1, pp. 25-39, 2016.
- Hadi Hadizadeh, Mario J. Enriquez and Ivan V. Bajic, “EyeTracking Database for a Set of Standard Video Sequences”, IEEE Transactions on Image Processing, Vol. 21, No. 2, pp.898-903, 2012.
- M. Xu, L. Jiang, X. Sun, Z. Ye and Z. Wang, “Learning to Detect Video Saliency With HEVC Features”, IEEE Transactions on Image Processing, Vol. 26, No. 1, pp. 369385, 2017.
- G. Sullivan, J. Ohm, W.J. Han, and T. Wiegand.“Overview of the High Efficiency Video Coding (HEVC) Standard”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 22, No. 12, pp. 1649-1668, 2012.
- T. Wiegand, G.J. Sullivan, G. Bjontegaard and A. Luthra, “Overview of the H. 264/AVC Video Coding Standard”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 13, No. 7, pp. 560-576, 2003.
- M.G. Arvanitidou, A. Glantz, A. Krutz, T. Sikora, M. Mrak and A. Kondoz, “Global Motion Estimation using Variable Block Sizes and its Application to Object Segmentation”, Proceedings of 10th Workshop on Image Analysis for Multimedia Interactive Services, pp. 173-176, 2009.
- L. Itti,“Automatic foveation for Video Compression using a Neurobiological Model of Visual Attention”, IEEE Transactions on Image Processing, Vol. 13, No. 10, pp.1304-1318, 2004.
- L. Itti, C. Koch and E. Niebur,“A Model of Saliency-based Visual Attention for Rapid Scene Analysis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 11, pp. 1254-1259, 1998.
- L. Itti and P. Baldi, “Bayesian Surprise Attracts Human Attention”, Vision Research, Vol. 49, No. 10, pp. 12951306, 2009.
- T. Judd, K. Ehinger, F. Durand and A. Torralba, “Learning to Predict where Humans Look”, Proceedings of 10th International Conference on Computer Vision, pp. 21062113, 2009.
- C. Guo and L. Zhang, “A Novel Multiresolution Spatiotemporal Saliency Detection Model and its Applications in Image and Video Compression”, IEEE Transactions on Image Processing, Vol. 19, No. 1, pp. 185198, 2010.
- D. Rudoy, D.B. Goldman, E. Shechtman, and L. ZelnikManor, “Learning Video Saliency from Human Gaze using Candidate Selection”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1147-1154, 2013.
- Y. Fang, W. Lin, Z. Chen, C.M. Tsai and C.W. Lin, “A Video Saliency Detection Model in Compressed Domain”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 24, No. 1, pp. 27-38, 2014.
- S. Hossein Khatoonabadi, N. Vasconcelos, I.V. Bajic and Y. Shan, “How Many Bits does it take for a Stimulus to be Salient?”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5501-5510, 2015.
- Mai Xu, Xin Deng, Shengxi Li and Zulin Wang, “Regionof-Interest based Conversational HEVC Coding with Hierarchical Perception Model of Face”, IEEE Journal of Selected Topics in Signal Processing, Vol. 8, No. 3, pp. 475489, 2014.
- J.R. Ohm, G.J. Sullivan, H. Schwarz, T.K. Tan and T. Wiegand, “Comparison of the Coding Efficiency of Video Coding Standards-Including High Efficiency Video Coding (HEVC)”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 22, No. 12, pp. 1669-1684, 2012.
- Ali Borji and Laurent Itti, “State-of-the-Art in Visual Attention Modeling”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 1, pp. 185207, 2013.
- Ali Borji, Dicky N. Sihite and Laurent Itti, “Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study”, IEEE Transactions on Image Processing, Vol. 22, No. 1, pp. 55-69, 2013.
- L. Itti and P. Baldi, “A Principled Approach to Detecting Surprising Events in Video”, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 631-637, 2005.
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