Adaptive Compression of Stereoscopic Images
Nowadays the growing availability of stereo cameras for common applications is becoming a commodity. This paper addresses the problem of stereoscopic images data compression proposing an innovative algorithm for compressing Multi Picture Object coded stereopairs. By means of self organizing reconstruction algorithm based on image redundancy we are able to reduce the size of the enclosed JPEG images. The overall perceived (and measured) quality is managed by considering that a stereoscopic image represents the same scene acquired from two different perspectives. In particular we achieve some compression gain just encoding the two images with different quality factors. The reported results and test benchmarks show the robustness and efficiency of the proposed algorithm.
Unable to display preview. Download preview PDF.
- 1.Anderson, P.: Advanced Display Technologies JISC Technology and Standards Watch. JISC, Bristol (2005)Google Scholar
- 2.Wikipedia contributors, Polarized 3D system, Wikipedia The Free Encyclopedia (2013), http://en.wikipedia.org/wiki/Polarized_3D_system
- 3.Multi-Picture format, Camera & Imaging Products Association Standardization Committee, CIPA (2009)Google Scholar
- 6.Battiato, S., Mancuso, M., Bosco, A., Guarnera, M.: Psychovisual and Statistical Optimization of Quantization Tables for DCT Compression Engines. In: Proceedings of IEEE International Conference on Image Analysis and Processing, pp. 602–606 (2001)Google Scholar
- 8.Schenkel, M.B., Luo, C., Frossard, P., Wu, F.: Joint decoding of stereo JPEG image Pairs. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 2633–2636 (2010)Google Scholar
- 9.Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, Upper Saddle River (2008)Google Scholar
- 10.3DMedia – 3D Technology and Software (2013), http://www.3dmedia.com/gallery
- 12.Briechle, K., Hanebeck, U.D.: Template matching using fast normalized cross correlation. In: International Society for Optics and Photonics, Aerospace/Defense Sensing, Simulation, and Controls, pp. 95–102 (2001)Google Scholar
- 15.Banitalebi-Dehkordi, A., Pourazad, M.T., Nasiopoulos, P.: A human visual system based 3D video quality metric. In: The 2nd International Conference on 3D Imaging, IC3D, Liege, Belgium (2012)Google Scholar