zbMATH — the first resource for mathematics

A bit rate adaptation model for 3D video. (English) Zbl 1380.94030
Summary: Although a significant research effort has been carried out for investigating 3 Dimensional (3D) video display, transmission, coding, etc, the same is not applicable for 3D video adaptation. In addition, ambient illumination, spatial resolution and 3D video content related contexts have not been particularly considered in a hybrid manner for the 3D video adaptation purpose in literature to date. In this paper, an adaptation decision taking technique is designed to predict the bit rate of 3D video sequences to be adapted by a proposed adaptation model. The ambient illumination condition of the viewing environment is considered in these proposed technique and model together with spatial resolution, video quality, and depth perception related contexts of the 3D video. Experimental results derived by the assistance of subjective experiments prove that the proposed model is quite efficient to adapt the 3D video sequences without compromising the 3D video perception of the users.
94A08 Image processing (compression, reconstruction, etc.) in information and communication theory
Full Text: DOI
[1] Devore, J.L. (1995). Probability and statistics for engineering and the sciences. Duxbury Press.
[2] Fleet, DJ; Wiess, Y, Optical flow estimation in paragios, 239, (2006), Berlin
[3] Frazor, RA; Geisler, WS, Local luminance and contrast in natural images, Elsevier Vision Research Journal, 46, 1585-1598, (2006) · Zbl 1185.37091
[4] Geisler, WS, Visual perception and the statistical properties of natural scenes, Review of Psychology, 59, 167-192, (2008)
[5] Gretag Macbeth Eye-One Display 2 (Online). http://www.xrite.com
[6] Grigorescu, C; Petkov, N; Westenberg, MA, Contour and boundary detection improved by surround suppression of texture edges, Image and Vision Computing, 22, 609-622, (2004)
[7] Hall, P; Wand, MP, On the minimization of absolute distance in kernel density estimation, Statistics and Probability Letters, 6, 311-314, (1988) · Zbl 0629.62037
[8] Ichihara, S., Kitagawa, N., & Akutsu, H. (2007). Contrast and depth perception: Effects of texture contrast and area contrast. Perception, 36(5), 686-695.
[9] Jones, V. (2009). Mean direction and mean absolute deviation. ASTM Standards and Engineering Digital Library.
[10] JSVM 9.13.1. CVS Server (Online). Available Telnet: garcon.ient.rwth aachen.de:/cvs/jvt.
[11] Kim, MB; Nam, J; Baek, W; Son, J; Hong, J, The adaptation of 3D stereoscopic video in MPEG-21 DIA, special issue on mul. ad., Signal Processing: Image Communication, 18, 685-697, (2003)
[12] Liu, Y., Ci, S., Liu, J., & Qi, Y. (Nov.2012). Integrating stereoscopic image transcoding with retargeting for mobile streaming. In Visual communications and image processing conference 2012 (VCIP 2012) (pp. 27-30). San Diego, CA, USA.
[13] Liu, Y., Ci, S., Tang, H., Ye, Y., & Liu, J. (2012). QoE-oriented 3D video transcoding for mobile streaming. ACM Transactions on Multimedia Computing, Communications and Applications (TOMCCAP), \(8\)(3). Article 42.
[14] Malik, J; Belongie, S; Leung, T; Shi, J, Contour and texture analysis for image sequenceation, International Journal of Computer Vision, 1, 7-27, (2001) · Zbl 0972.68604
[15] Mathworks curve fitting toolbox (2013). http://www.mathworks.com/access/helpdesk/help/toolbox/curvefit/
[16] Methodology for the subjective assessment of the quality of television pictures, ITU-R BT.500-13 (2012).
[17] Nur, G., Dogan, S., Kodikara Arachchi, H., & Kondoz, A. M. (1-3 Sep. 2010). Assessing the effects of ambient illumination change in usage environment on 3D video perception for user centric media access and consumption. In 2nd International ICST conference on user centric media, Palma de Mallorca, Spain.
[18] Nur, G., Dogan, S., Kodikara Arachchi, H., & Kondoz, A. M. (16-18 May 2011). Extended VQM model for predicting 3D video quality considering ambient illumination context. IEEE 3DTV conference: the true vision-capture, transmission and display of 3D video, Antalya, Turkey.
[19] Nur, G., Kodikara Arachchi, H., Dogan, S., & Kondoz, A. M. (10-12 June 2009). A novel scalability adaptation concept for higher R-D performances of scalable video. ICT Mobile and Wireless Communications Summit, Santander, Spain.
[20] Nur, G; Kodikara Arachchi, H; Dogan, S; Kondoz, AM, Ambient illumination as a context for video bit rate adaptation decision taking, IEEE TCSVT, 20, 1887-1891, (2010)
[21] Nur, G; Kodikara Arachchi, H; Dogan, S; Kondoz, A, Advanced adaptation techniques for improved video perception, IEEE Transactions on Circuits and Systems for Video Technology, 22, 225-240, (2012)
[22] Nur, G; Kodikara Arachchi, H; Dogan, S; Kondoz, AM, Modeling user perception of 3D video based on ambient illumination context for enhanced user centric media access and consumption, Multimedia Tools and Applications Journal Special Issue on User Centric Media, 70, 333-359, (2014)
[23] Shi, J., & Tomasi, C. (Jun. 1994). Good features to track. IEEE conference on computer vision and pattern recoginition, Seattle, USA.
[24] Thang, T. C., Kang, J. W., Yoo, J.-J., & Kim, J.-G. (26-31 Oct. 2008). Multilayer adaptation for MGS-based SVC bitstream. In: Proceedings of the 16th ACM multimedia, Vancouver, British Columbia, Canada.
[25] Wang, Z., Bovik, A., Sheikh, H., & Simoncelli, E. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on image processing, 13(4), 600-612.
[26] Wolfram mathworld correlation coefficient. http://mathworld.wolfram.com/CorrelationCoefficient.html
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.