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Proceedings Paper

Data compression of stereoscopic image pairs
Author(s): Changman Xu; Zhaoyang Zhang
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Paper Abstract

An emerging feature of multimedia and telepresence systems is stereo imagery. Stereo images provide an enhanced sense of presence, and have been found to be operationally useful in tasks requiring remote manipulation or judgment of spatial relationships. A conventional stereo system with a single left-right pair needs twice the raw data as a monoscopic imaging system. As a result there has been increasing attention given to image compression methods specialized to stereo pairs. In this paper, the mixed-resolution coding, is a psychophysically justified technique that exploits known facts about human stereovision to code stereopairs in a subjectively acceptable manner, is used to the stereo image compression. By combining both the mixed-resolution coding and SPT(subspace projection technique)-based disparity-compensation techniques, the left image can be compressed by a wavelet-transform-based scheme independent of the right image. By performing low-resolution SPT-based disparity-compensation technique, the disparity is able to predict the low-resolution right image from the left image at a lower resolution using the disparity relation. The low-resolution images are obtained using the wavelet decomposition. At the decoder, the low-resolution right subimage is estimated using the disparity from the low-resolution left subimage. A full-sized resolution is obtained by upsampling with a factor of 4 and resonstructing with the synthesis low pass filter. We provide experimental results, that show that our proposed scheme achieves a PSNR gain of about 0.98 dB as compared to a block-based disparity compensation coding scheme, encoded at the same bit rate.

Paper Details

Date Published: 26 September 2001
PDF: 5 pages
Proc. SPIE 4551, Image Compression and Encryption Technologies, (26 September 2001); doi: 10.1117/12.442894
Show Author Affiliations
Changman Xu, Shanghai Univ. (China)
Zhaoyang Zhang, Shanghai Univ. (China)

Published in SPIE Proceedings Vol. 4551:
Image Compression and Encryption Technologies
Jun Tian; Tieniu Tan; Liangpei Zhang, Editor(s)

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