Subsource-based compression in remote sensing

Abstract Classical compression methods of remote sensing (RS) panchromatic images are much the same as the traditional compression ones, in which distributions of different surface features are not taken into account. Instead, RS panchromatic images are divided into blocks in our method and those blocks can be classified into several categories by analyzing their intensity distributions. Afterwards, each category is compressed separately. According to Shannon’s theorem 3, a source with given distribution and distortion has a unique theoretical minimum bitrate. Hence, under a given compression quality, the theoretical minimum bitrate of each category can be calculated using rate-distortion theory. Meanwhile, each category may have its own distortion due to the user’s different quality requirements. Our method performs well in reducing the redundancy of surface features which users do not care about so that more “valid data” would be obtained from the compressed images. Furthermore, it also provides flexibility between fixed compression ratio and quality-based compression.

[1]  Chengjun Liu,et al.  Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition , 2002, IEEE Trans. Image Process..

[2]  Mohammed Ghanbari,et al.  Scope of validity of PSNR in image/video quality assessment , 2008 .

[3]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[4]  Richard W. Hamming,et al.  Coding and Information Theory , 1980 .

[5]  K. J. Ray Liu,et al.  Anti-forensics of digital image compression , 2011, IEEE Transactions on Information Forensics and Security.

[6]  Ilias Maglogiannis,et al.  Wavelet-Based Compression With ROI Coding Support for Mobile Access to DICOM Images Over Heterogeneous Radio Networks , 2009, IEEE Transactions on Information Technology in Biomedicine.

[7]  Antonio Ortega,et al.  Rate-distortion methods for image and video compression , 1998, IEEE Signal Process. Mag..

[8]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[9]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Bo Li,et al.  Remote-Sensing Image Compression Using Two-Dimensional Oriented Wavelet Transform , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[11]  I. Horev,et al.  Adaptive image compression using sparse dictionaries , 2012, 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP).

[12]  Jerzy A. Seidler.  Information Systems and Data Compression , 1997, Springer US.

[13]  Andrew Zisserman,et al.  A Statistical Approach to Texture Classification from Single Images , 2004, International Journal of Computer Vision.

[14]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[15]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[16]  Zhaoda Zhu,et al.  Low Bit Rate ROI-Based SAR Image Compression , 2009, 2009 Fifth International Conference on Information Assurance and Security.

[17]  Philip H. Enslow.  What is a "Distributed" Data Processing System? , 1978, Computer.

[18]  William A. Pearlman,et al.  A new, fast, and efficient image codec based on set partitioning in hierarchical trees , 1996, IEEE Trans. Circuits Syst. Video Technol..

[19]  Thanos Stouraitis,et al.  A computational efficient algorithm for adaptive image compression , 1995 .

[20]  Sumihisa Hashiguchi,et al.  Coding characteristics of the pixel-adaptive DPCM utilizing the variable-length-codes , 1991 .

[21]  Song Xue,et al.  A remote sensing image compression method suited to space-borne application , 2011, Proceedings of 2011 International Conference on Computer Science and Network Technology.

[22]  Olga Kosheleva,et al.  Application of task-specific metrics in JPEG2000 ROI compression , 2002, Proceedings Fifth IEEE Southwest Symposium on Image Analysis and Interpretation.

[23]  Giuliano Benelli,et al.  A DCT-based adaptive compression algorithm customized for radar imagery , 1997, IGARSS'97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing - A Scientific Vision for Sustainable Development.

[24]  Majid Rabbani,et al.  An overview of the JPEG 2000 still image compression standard , 2002, Signal Process. Image Commun..

[25]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[26]  Jerome M. Shapiro,et al.  Embedded image coding using zerotrees of wavelet coefficients , 1993, IEEE Trans. Signal Process..