Adaptive stereo image joint compression based on characteristics classification

Shizhong Li; Zhe Chen; Xiaofeng Liu

doi:10.1117/12.901872

8 December 2011 Adaptive stereo image joint compression based on characteristics classification

Shizhong Li, Zhe Chen, Xiaofeng Liu

Proceedings Volume 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis; 800218 (2011) https://doi.org/10.1117/12.901872
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

On the basis of analyzing the characteristics of the stereo image pair obtained by the remote sensing mapping, considering the continual increment of data obtained by stereo mapping satellite on orbit and the trend of the hardware development, a joint compression algorithm for the stereo image pairs based on the classified characters is proposed. First, stereo image pairs are matched through SIFT feature matching; and then the images are classified adaptively by the features according to the matching results. For the region where the characteristics are flat, the predicted residual-error image is coded based on the coarse match and the fine match using the adaptive block in order to improve the accuracy of matching with the linear prediction for the radiation compensation. For the feature-rich region, according to the correlation of the images, individual compression or joint compression is selected. When the joint compression algorithm is selected, the match alignment of the left and right image pair is done by the affine model estimation and the linear prediction is adapted to the radiation compensation. Because different compression scheme is adopted for different characteristics regions, the results show that the PSNR and the geometric precision of the reconstructed images are improved effectively, especially when the images contain many characteristics of elevation information.

Citation Download Citation

Shizhong Li, Zhe Chen, and Xiaofeng Liu "Adaptive stereo image joint compression based on characteristics classification", Proc. SPIE 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis, 800218 (8 December 2011); https://doi.org/10.1117/12.901872

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KEYWORDS

Image compression

Image quality

JPEG2000

Image classification

Image restoration

Remote sensing

Affine motion model

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