Adaptive Stereo Image Joint Compression Based on Characteristics Classification
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.
surveying and mapping satellite characteristics classification joint compression disparity estimation radiation compensation compression efficiency PSNR geometric precision
Shizhong Li Zhe Chen Xiaofeng Liu Zhaochun Ran
School of Earth ＆ Spacea Sciences, Peking University, Beijing, P.R.China, 100871 School of Information Institute, Chang’an University, Xi’an, P.R.China, 710064 Communication Research Center, Harbin Instiute of Technology, Harbin, P.R.China, 150001 Hainan Technology and Business College, Hainan, P.R.China, 570203
国际会议
桂林
英文
1-9
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）