An Object of Interest based Segmentation Approach for Selective Compression of Video Frames
Автор: Marykutty Cyriac, Sankar. P.
Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp
Статья в выпуске: 2 vol.8, 2016 года.
Бесплатный доступ
The automatic segmentation of objects of interest is a new research area with applications in various fields. In this paper, the object segmentation method is used for content based video management and compression of video frames for video conferencing. The face region, which is the object of interest in the video frames, is identified first using a skin color based algorithm. The face regions are then extracted and encoded without loss, while the non- face regions and the non-face frames are quantized before encoding. Results show that the decompressed video has an improved quality with the proposed approach at low bit rates.
Object of interest, segmentation, face detection, skin detection, video compression
Короткий адрес: https://sciup.org/15013950
IDR: 15013950
Список литературы An Object of Interest based Segmentation Approach for Selective Compression of Video Frames
- Fernandez, IA, Rondao AP, Tong G, Lauwereins. R, De Vleeschouwer C, "Integrated H.264 region-of-interest detection, tracking and compression for surveillance scenes", Packet Video Workshop (PV), 2010, 18th Inter-national , vol.17,no. 24, pp.13-14,2010.
- Minghui W, Tianruo Z, Chen Liu, Goto S, "Region-of-interest based H.264 encoding parameter allocation for low power video communication, Signal Processing & Its Applications", 5th International Colloquium on, pp. 233-237,2009.
- Chaoqiang L, Tao X, Hui L ."ROI and FOI Algorithms for Wavelet-Based Video Compression", Advances in Multimedia Information Processing, PCM 2004, Lecture Notes in Computer Science 3333, pp.241-248, 2004.
- Jatoth R.J, Gopisetty.S, Hussain.M, "Performance Analysis of Alpha Beta Filter, Kalman Filter and Meanshift for Object Tracking in Video Sequences", International Journal of Image, Graphics and Signal Processing, vol.7, no.3, pp.24-30, 2015.
- Byoung C.K and Jae-Yeal N , "Object-of-interest image segmentation based on human attention and semantic region clustering", J. Opt. Soc. Am. vol. A / 23(10), pp. 2462-2470, 2006.
- Esa R, Juho Ka, Mikko S, and Janne H, "Segmenting salient objects from images and videos", In Proceedings of the 11th European conference on Computer vision (ECCV'10), Springer-Verlag, Berlin, Heidelberg, pp.366-379,2010.
- Niu C, Liu Y, " Moving Object Segmentation in the H.264 Compressed Domain", ACCV 2009, Lecture Notes in Computer Science, vol. 5995, 2009,pp. 645-654.
- Lee Y.J, Kim J, Grauman K, "Key-segments for video object segmentation", IEEE International Conference on Computer Vision , pp. 6-13, 2011
- Jiebo L, Chang W.C, and Kevin J.P, "Face Location in Wavelet-Based Video Compression for High Perceptual Quality Video-conferencing", IEEE Transactions on Circuits and Systems for Video Technology, vol. 6, no.4, pp.411-415, 1996.
- Ali M.S, Ahmeed S.F, Loay E.G, "Fast Intra-frame compression for Video Conferencing using Adaptive Shift Coding", International Journal of Computer Applications ,vol. 81, no. 8, pp. 29-33,2013.
- Zeynep O, Abdulkadir B, Erdem K, "A Study On Face, Eye Detection And Gaze Estimation", International Journal of Computer Science & Engineering Survey, vol 2, no.3, pp.29-46, 2011.
- Tao G, "High-precision Immune Computation for Secure Face Recognition", International Journal of Security and Its Applications, vol. 6, no. 2, pp.293-298,2012.
- Sasikumar G and Tripathy B.K, "Design and Implementation of Face Recognition System in Matlab Using the Features of Lips, International Journal of Intelligent Systems and Applications, vol 8,pp.30-36 .2011.
- Taskeed J, Kabir M.H, Oksam C,"Robust Facial Expression Recognition Based on Local Directional Pattern", ETRI Journal, vol. 32, no.5, pp.784-794, 2010.
- Dong-Ju K, Lee S and Sohn M.K, "Face Recognition via Local Directional Pattern", International Journal of Security and its Applications, vol.7, no.2, pp.191-200,2013.
- Yasaman H, Abolfazl T.H, "An Efficient Face Detection Method Using Adaboost and Facial Parts", IJSSST, vol 12. no.4, pp.473-804,2011.
- Lee Y, Han D.K, Ko H (2013), Reinforced AdaBoost Learning for Object Detection with Local Pattern Representations, The Scientific World Journal, Article ID 153465, doi.org/10.1155/2013/153465.
- Ghimire D and Lee H, "Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines", Sensors, vol.13, pp.7714-7734, 2013, doi:10.3390/s130607714.
- Baskoro H, Kim J.S, Kim C.S, "Mean-Shift Object Tracking with Discrete and Real AdaBoost Techniques", ETRI Journal, vol. 31, no.3, pp. 282-292, 2013.
- Kim K, Kang S, Chi S and Jaehong Kim, "Object Recognition on Horse Riding Simulator System", World Academy of Science, Engineering and Technology,vol.77, pp. 05-23,2009.
- Turk M and Pentland A, "Eigenfaces for recognition" , Journal of Cognitive Neuroscience archive, vol.3, no.11, pp.71-86,1991.
- Slavković M and Jevtić D, "Face Recognition Using Eigenface Approach", Serbian Journal of Electrical Engineering , vol. 9, no.1, pp.121-130,2012.
- Alireza Tofighi, Khairdoost. N., S. Amirhassan Monadjemi and Kamal Jamshidi, "A Robust Face Recognition System in Image and Video", International Journal of Image, Graphics and Signal Processing, vol.6, no. 8, pp.1-11, 2014.
- Prashanth K.G, Shashidhara M, "Real Time Detection and Tracking of Human Face using Skin Color Segmentation and Region Properties", International Journal of Image, Graphics and Signal Processing, vol. 6, no. 8, pp..40-46, 2014.
- R.L Hsu, M.A .Mottaleb,A.K.Jain, Face detection in color Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, 696-706, 2002.
- Face image database http://www.prima.inrialpes.fr/FGnet/html/benchmarks.html
- http://www.ee.surrey.ac.uk/
Статья научная
