Multispectral image compression using the wavelet transform

Multispectral images contain information from several spectral wavelengths and currently multispectral images are widely used in remote sensing and they are becoming more common in the field of computer vision and in industrial applications. Typically, one multispectral image in remote sensing may occupy hundreds of megabytes of disk space and several this kind of images may be received from a single measurement. This study considers the compression of multispectral images. The lossy compression is based on the wavelet transform and we compare the suitability of different waveletfilters for the compression. A method for selecting a wavelet filter for the compression and reconstruction of multispectral images is developed. The performance of the multidimensional wavelet transform based compression is compared to other compression methods like PCA, ICA, SPIHT, and DCT/JPEG. The quality of the compression and reconstruction is measured by quantitative measures like signal-to-noise ratio. In addition, we have developed a qualitative measure, which combines the information from the spatial and spectral dimensions of a multispectral image and which also accounts for the visual quality of the bands from the multispectral images.

Lossless compression of hyperspectral images

Technological progress has made a huge amount of data available at increasing spatial and spectral resolutions. Therefore, the compression of hyperspectral data is an area of active research. In somefields, the original quality of a hyperspectral image cannot be compromised andin these cases, lossless compression is mandatory. The main goal of this thesisis to provide improved methods for the lossless compression of hyperspectral images. Both prediction- and transform-based methods are studied. Two kinds of prediction based methods are being studied. In the first method the spectra of a hyperspectral image are first clustered and and an optimized linear predictor is calculated for each cluster. In the second prediction method linear prediction coefficients are not fixed but are recalculated for each pixel. A parallel implementation of the above-mentioned linear prediction method is also presented. Also,two transform-based methods are being presented. Vector Quantization (VQ) was used together with a new coding of the residual image. In addition we have developed a new back end for a compression method utilizing Principal Component Analysis (PCA) and Integer Wavelet Transform (IWT). The performance of the compressionmethods are compared to that of other compression methods. The results show that the proposed linear prediction methods outperform the previous methods. In addition, a novel fast exact nearest-neighbor search method is developed. The search method is used to speed up the Linde-Buzo-Gray (LBG) clustering method.

Wavelet Filter Parameterization for Compression of Spectral Images

Vaatimus kuvatiedon tiivistämisestä on tullut entistä ilmeisemmäksi viimeisen kymmenen vuoden aikana kuvatietoon perustuvien sovellutusten myötä. Nykyisin kiinnitetään erityistä huomiota spektrikuviin, joiden tallettaminen ja siirto vaativat runsaasti levytilaa ja kaistaa. Aallokemuunnos on osoittautunut hyväksi ratkaisuksi häviöllisessä tiedontiivistämisessä. Sen toteutus alikaistakoodauksessa perustuu aallokesuodattimiin ja ongelmana on sopivan aallokesuodattimen valinta erilaisille tiivistettäville kuville. Tässä työssä esitetään katsaus tiivistysmenetelmiin, jotka perustuvat aallokemuunnokseen. Ortogonaalisten suodattimien määritys parametrisoimalla on työn painopisteenä. Työssä todetaan myös kahden erilaisen lähestymistavan samanlaisuus algebrallisten yhtälöiden avulla. Kokeellinen osa sisältää joukon testejä, joilla perustellaan parametrisoinnin tarvetta. Erilaisille kuville tarvitaan erilaisia suodattimia sekä erilaiset tiivistyskertoimet saavutetaan eri suodattimilla. Lopuksi toteutetaan spektrikuvien tiivistys aallokemuunnoksen avulla.

Classification-Based compression of Multispectral Images

The purpose of this thesis is to present a new approach to the lossy compression of multispectral images. Proposed algorithm is based on combination of quantization and clustering. Clustering was investigated for compression of the spatial dimension and the vector quantization was applied for spectral dimension compression. Presenting algo¬rithms proposes to compress multispectral images in two stages. During the first stage we define the classes' etalons, another words to each uniform areas are located inside the image the number of class is given. And if there are the pixels are not yet assigned to some of the clusters then it doing during the second; pass and assign to the closest eta¬lons. Finally a compressed image is represented with a flat index image pointing to a codebook with etalons. The decompression stage is instant too. The proposed method described in this paper has been tested on different satellite multispectral images from different resources. The numerical results and illustrative examples of the method are represented too.

Lossless Compression Methods for Multispectral Images: Development and Comparisons

Main purpose of this thesis is to introduce a new lossless compression algorithm for multispectral images. Proposed algorithm is based on reducing the band ordering problem to the problem of finding a minimum spanning tree in a weighted directed graph, where set of the graph vertices corresponds to multispectral image bands and the arcs’ weights have been computed using a newly invented adaptive linear prediction model. The adaptive prediction model is an extended unification of 2–and 4–neighbour pixel context linear prediction schemes. The algorithm provides individual prediction of each image band using the optimal prediction scheme, defined by the adaptive prediction model and the optimal predicting band suggested by minimum spanning tree. Its efficiency has been compared with respect to the best lossless compression algorithms for multispectral images. Three recently invented algorithms have been considered. Numerical results produced by these algorithms allow concluding that adaptive prediction based algorithm is the best one for lossless compression of multispectral images. Real multispectral data captured from an airplane have been used for the testing.

Integrin-Specific Mechanoresponses to Compression and Extension Probed by Cylindrical Flat-Ended AFM Tips in Lung Cells

Cells from lung and other tissues are subjected to forces of opposing directions that are largely transmitted through integrin-mediated adhesions. How cells respond to force bidirectionality remains ill defined. To address this question, we nanofabricated flat-ended cylindrical Atomic Force Microscopy (AFM) tips with ~1 µm2 cross-section area. Tips were uncoated or coated with either integrin-specific (RGD) or non-specific (RGE/BSA) molecules, brought into contact with lung epithelial cells or fibroblasts for 30 s to form focal adhesion precursors, and used to probe cell resistance to deformation in compression and extension. We found that cell resistance to compression was globally higher than to extension regardless of the tip coating. In contrast, both tip-cell adhesion strength and resistance to compression and extension were the highest when probed at integrin-specific adhesions. These integrin-specific mechanoresponses required an intact actin cytoskeleton, and were dependent on tyrosine phosphatases and Ca2+ signaling. Cell asymmetric mechanoresponse to compression and extension remained after 5 minutes of tip-cell adhesion, revealing that asymmetric resistance to force directionality is an intrinsic property of lung cells, as in most soft tissues. Our findings provide new insights on how lung cells probe the mechanochemical properties of the microenvironment, an important process for migration, repair and tissue homeostasis.

Labor Market Signaling and Self-Confidence: Wage Compression and the Gender Pay Gap

I extend Spence's signaling model by assuming that some workers are overconfident-they underestimate their marginal cost of acquiring education-and some are underconfident. Firms cannot observe workers' productive abilities and beliefs but know the fractions of high-ability, overconfident, and underconfident workers. I find that biased beliefs lower the wage spread and compress the wages of unbiased workers. I show that gender differences in self-confidence can contribute to the gender pay gap. If education raises productivity, men are overconfident, and women underconfident, then women will, on average, earn less than men. Finally, I show that biased beliefs can improve welfare.

Determination of compression behaviour of coating layer

The strength properties of paper coating layer are very important in converting and printing operations. Too great or low strength of the coating can affect several problems in printing. One of the problems caused by the strength of coating is the cracking at the fold. After printing the paper is folded to final form and the pages are stapled together. In folding the paper coating can crack causing aesthetic damage over printed image or in the worst case the centre sheet can fall off in stapling. When folding the paper other side undergoes tensile stresses and the other side compressive stresses. If the difference between these stresses is too high, the coating can crack on the folding. To better predict and prevent cracking at the fold it is good to know the strength properties of coating layer. It has measured earlier the tensile strength of coating layer but not the compressive strength. In this study it was tried to find some way to measure the compressive strength of the coating layer and investigate how different coatings behave in compression. It was used the short span crush test, which is used to measure the in-plane compressive strength of paperboards, to measure the compressive strength of the coating layer. In this method the free span of the specimen is very small which prevent buckling. It was measured the compressive strength of free coating films as well as coated paper. It was also measured the tensile strength and the Bendtsen air permeance of the coating film. The results showed that the shape of pigment has a great effect to the strength of coating. Platy pigment gave much better strength than round or needle-like pigment. On the other hand calcined kaolin, which is also platy but the particles are aggregated, decreased the strength substantially. The difference in the strength can be explained with packing of the particles which is affecting to the porosity and thus to the strength. The platy kaolin packs up much better than others and creates less porous structure. The results also showed that the binder properties have a great effect to the compressive strength of coating layer. The amount of latex and the glass transition temperature, Tg, affect to the strength. As the amount of latex is increasing, the strength of coating is increasing also. Larger amount of latex is binding the pigment particles better together and decreasing the porosity. Compressive strength was increasing when the Tg was increasing because the hard latex gives a stiffer and less elastic film than soft latex.

New Distance Transforms for Gray Level Image Compression

This thesis deals with distance transforms which are a fundamental issue in image processing and computer vision. In this thesis, two new distance transforms for gray level images are presented. As a new application for distance transforms, they are applied to gray level image compression. The new distance transforms are both new extensions of the well known distance transform algorithm developed by Rosenfeld, Pfaltz and Lay. With some modification their algorithm which calculates a distance transform on binary images with a chosen kernel has been made to calculate a chessboard like distance transform with integer numbers (DTOCS) and a real value distance transform (EDTOCS) on gray level images. Both distance transforms, the DTOCS and EDTOCS, require only two passes over the graylevel image and are extremely simple to implement. Only two image buffers are needed: The original gray level image and the binary image which defines the region(s) of calculation. No other image buffers are needed even if more than one iteration round is performed. For large neighborhoods and complicated images the two pass distance algorithm has to be applied to the image more than once, typically 3 10 times. Different types of kernels can be adopted. It is important to notice that no other existing transform calculates the same kind of distance map as the DTOCS. All the other gray weighted distance function, GRAYMAT etc. algorithms find the minimum path joining two points by the smallest sum of gray levels or weighting the distance values directly by the gray levels in some manner. The DTOCS does not weight them that way. The DTOCS gives a weighted version of the chessboard distance map. The weights are not constant, but gray value differences of the original image. The difference between the DTOCS map and other distance transforms for gray level images is shown. The difference between the DTOCS and EDTOCS is that the EDTOCS calculates these gray level differences in a different way. It propagates local Euclidean distances inside a kernel. Analytical derivations of some results concerning the DTOCS and the EDTOCS are presented. Commonly distance transforms are used for feature extraction in pattern recognition and learning. Their use in image compression is very rare. This thesis introduces a new application area for distance transforms. Three new image compression algorithms based on the DTOCS and one based on the EDTOCS are presented. Control points, i.e. points that are considered fundamental for the reconstruction of the image, are selected from the gray level image using the DTOCS and the EDTOCS. The first group of methods select the maximas of the distance image to new control points and the second group of methods compare the DTOCS distance to binary image chessboard distance. The effect of applying threshold masks of different sizes along the threshold boundaries is studied. The time complexity of the compression algorithms is analyzed both analytically and experimentally. It is shown that the time complexity of the algorithms is independent of the number of control points, i.e. the compression ratio. Also a new morphological image decompression scheme is presented, the 8 kernels' method. Several decompressed images are presented. The best results are obtained using the Delaunay triangulation. The obtained image quality equals that of the DCT images with a 4 x 4