https://www.cs.uleth.ca/~cheng/papers/prletters1997.pdf

Image compression and encryption using tree structures

Remote sensing images contain huge amount of geographical information and reflect the complexity of geographical features and spatial structures. As the means of observing and describing geographical phenomena, the rapid development of remote sensing has provided an enormous amount of geographical information. The massive information is very useful in a variety of applications but the sheer bulk of this information has increased beyond what can be analyzed and used efficiently and effectively. This uneven increase in the technologies of gathering and analyzing information has created difficulties in its storage, transfer, and processing. Fractal geometry provides a means of describing and analyzing the complexity of different geographical features in remotely sensed images. It also provides a more powerful tool to compress the remote sensing data than traditional methods. This study suggests, for the first time, the implementation of this usage of fractals to remotely sensed images. 
In this study, based on fractal concepts, compression and decompression algorithms were developed and applied to Landsat TM images of eight study areas with different land cover types; the fidelity and efficiency of the algorithms and their relationship with the spatial complexity of the images were evaluated. Three research hypotheses were tested and the fractal compression was compared with two commonly used compression methods, JPEG and WinZip. The effects of spatial complexity and pixel resolution on the compression rate were also examined. 
The results from this study show that the fractal compression method has higher compression rate than JPEG and WinZip. As expected, higher compression rates were obtained from images of lower complexity and from images of lower spatial resolution (larger pixel size). This study shows that in addition to the fractal's use in measuring, describing, and simulating the roughness of landscapes in geography, fractal techniques were useful in remotely sensed image compression. Moreover, the compression technique can be seen as a new method of measuring the diverse landscapes and geographical features. As such, this study has introduced a new and advantageous passageway for fractal applications and their important applications in remote sensing.

/pdf/fractal-compression-and-analysis-on-remotely-sensed-imagery-2z744k60bm.pdf

Fractal Compression and Analysis on Remotely Sensed Imagery

A new type of scene adaptive coder has been developed. The described coder involves a quadtree mean decomposition of the motion compensated frame-to-frame difference signal followed by a scalar quantization of the local means. As a fundamental property, the new coding algorithm treats the displacement estimation problem and the quadtree construction problem as a unit. Excellent results have been obtained for coding of color image sequences at a rate of 64 kbits/s.

Tree-structured scene adaptive coder

Image representation plays a key role in optimizing image processing algorithms. A new image representation method is presented, which is called Inclined plane Decomposition based Non-symmetry and Anti-packing image representation Method (IDNAM), in order to improve the efficiency of image processing algorithms. An algorithm is given for an abstract image processing based on the IDNAM, and the algorithm generalizes the basic idea of image processing algorithms based on the IDNAM. The theoretical analyses show that the time complexity of this algorithm is smaller than that of the abstract image processing algorithms based on pixel representation.

A new method for image representation

Remote sensing images contain huge amount of geographical information and reflect the complexity of geographical features and spatial structures. As the means of observing and describing geographical phenomena, the rapid development of remote sensing has provided an enormous amount of geographical information. The massive information is very useful in a variety of applications but the sheer bulk of this information has increased beyond what can be analyzed and used efficiently and effectively. This uneven increase in the technologies of gathering and analyzing information has created difficulties in its storage, transfer, and processing. Fractal geometry provides a means of describing and analyzing the complexity of different geographical features in remotely sensed images. It also provides a more powerful tool to compress the remote sensing data than traditional methods. This study suggests, for the first time, the implementation of this usage of fractals to remotely sensed images. In this study, based on fractal concepts, compression and decompression algorithms were developed and applied to Landsat TM images of eight study areas with different land cover types; the fidelity and efficiency of the algorithms and their relationship with the spatial complexity of the images were evaluated. Three research hypotheses were tested and the fractal compression was compared with two commonly used compression methods, JPEG and WinZip. The effects of spatial complexity and pixel resolution on the compression rate were also examined. The results from this study show that the fractal compression method has higher compression rate than JPEG and WinZip. As expected, higher compression rates were obtained from images of lower complexity and from images of lower spatial resolution (larger pixel size). This study shows that in addition to the fractal’s use in measuring, describing, and simulating the roughness of landscapes in geography, fractal techniques were useful in remotely sensed image compression. Moreover, the compression technique can be seen as a new method of measuring the diverse landscapes and geographical features. As such, this study has introduced a new and advantageous passageway for fractal applications and their important applications in remote sensing. 

Fractal compression and analysis on remotely sensed imagery

Image compression schemes consist of compression and decompression phases. Often, the decompression phase includes mechanisms for improving the quality of the resulting image. Quadtree compression is one such type of compression. Reconstructed quadtree images can be blocky because of the nature of the compression method, unless additional processing, generally smoothing, is performed. We propose improvements over the reconstruction method described by Shusterman and Feder (see ibid., vol.3, p.207-15, 1994). The improvements consist of the introduction of a simpler and more effective 2/spl times/2 reconstruction filter to replace the suggested 3/spl times/3 filter, and the addition of a reconstruction threshold to preserve image edges. This method is compared experimentally to the original algorithm and also to JPEG compression in terms of complexity and resulting image quality.

On the reconstruction of quadtree data

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On the reconstruction of quadtree data