Weitere Kapitel dieses Buchs durch Wischen aufrufen
Wavelet foveated compression can be used in real-time video processing frameworks for reducing the communication overhead while keeping high visual quality. Such algorithm leads into high rate compression results due to the fact that the information loss is isolated outside a region of interest (ROI). The fovea compression can also be applied to other classic transforms such as the commonly used the discrete cosine transform (DCT). In this paper, a fovea window for wavelet-based compression is proposed. The proposed window allows isolate a fovea region over an image. A comparative analysis has been performed showing different error and compression rates between the proposed fovea window for wavelet-based and the DCT-based compression algorithms. Simulation results show that with foveated compression high ratio of compression can be achieved while keeping high quality over the designed ROI.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
N. Kehtarnavaz and M. Gamadia, “Real- Time Image and Video Processing: From Research to Reality,” Morgan and Claypool, University of Texas at Dallas, USA, 2006.
E. C. Chang and C. K. Yap, “A wavelet approach to foveating images,” In SCG’97: Proceedings of the thirteenth annual symposium on Computational geometry, New York, NY, USA, 1997, pp. 397–399. CrossRef
S. Lee and A. Bovik, “Fast algorithms for foveated video pro-cessing,” IEEE Transactions on Circuits and Systems for Video Technology, Vol.13, No. 2, 2003, pp. 149-162. CrossRef
Guo C, Zhang L (2010) A novel multiresolution spatiotemporal saliency detection model and Its applications in image and video compression. IEEE Trans Image Process 19(1):185–198
Boggess A, Narcowich FJ (2009) A first course in wavelets with Fourier analysis. 2nd edn. Wiley
Mallat S (2008) A wavelet tour of signal processing. In: The sparse way, 3rd edn. Academic Press
Ahmad J, Raza K, Ebrahim M, Talha U (2009) FPGA based implementation of baseline JPEG decoder. In: Proceedings of the 7th international conference on frontiers of information technology (FIT ’09). ACM, New York (Article 29)
N. Ahmed, T. Natarajan, and K. R. Rao, “Discrete cosine transform,” IEEE Transactions on Computers, Vol. C-32, January 1974, pp. 90-93.
Bovik AC (2009) The essential guide to image processing. Academic Press
B. A. Wandell. Foundations of Vision. Sinauer Associates, Inc., 1995.
Galan-Hernandez JC, Alarcon-Aquino V, Starostenko O, Ramirez-Cortes JM (2010) Wavelet-based foveated compression algorithm for real-time video processing. IEEE Electron Robotics Automot Mech Conf (CERMA′10) pp 405–410
Chang E (2000) Wavelet foveation. Appl Comput Harm Anal 9(3):312–335
C. Jain, V. Chaudhary, K. Jain, S. Karsoliya. “Performance analysis of integer wavelet transform for image compression,” 3rd International Conference on Electronics Computer Technology (ICECT′11), Vol.3, April 2011, pp.244-246. CrossRef
I. Bocharova, “ Compression for Multimedia,” 1st ed. Cambridge University Press, New York, NY, USA, 2010.
M. Mrak, M. Grgic, and M. Kunt, “High- Quality Visual Experience,” Signals and Comunication Technologiy Series, Springer-Verlag, Berlin, 2010. CrossRef
Richter T (2010) Spatial constant quantization in JPEG XR is nearly optimal. Data compression conference (DCC’10), March 2010, pp79–88
J. C. Galan-Hernandez, V. Alarcon-Aquino, O. Starostenko, and J. M. Ramirez-Cortes, “Foveated ROI compression with hierarchical trees for real-time video transmission,” In Proceedings of the Third Mexican conference on Pattern recognition (MCPR’11), Springer-Verlag, Berlin, Heidelberg, 2011, pp. 240-249.
- Fovea Window for Wavelet-Based Compression
J. C. Galan-Hernandez
J. M. Ramirez-Cortes
- Springer New York
- Chapter 55