The nonredundant contourlet transform (NRCT): a multiresolution and multidirection image representation with perfect reconstruction property

Multiresolution and multidirection image representation has recently been an attractive research area, in which multiresolution corresponds to varying scale of structure in images, while multidirection deals with the oriented nature of image structure. Numerous new systems, such as the contourlet transform, have been developed. The contourlet transform has the benefit of efficiently capturing the oriented geometrical structures of images; however, it has the drawback of a 4/3 redundancy in its oversampling ratio. In order to eliminate the redundancy, this thesis proposes a progressive version of the contourlet transform which can be calculated with critical sampling. The new proposed image representation is called the nonredundant contourlet transform (NRCT), which is constructed with an efficient framework of filter banks. In addition to critical sampling, the proposed NRCT possesses many valuable properties including perfect reconstruction, sparse expression, multiresolution, and multidirection. Numerical experiments demonstrate that the novel NRCT has better peak signal-to-noise performance than the traditional contourlet transform. Moreover, for low ratios of retained coefficients, the NRCT outperforms the wavelet transform which is a standard method for the critically sampled representation of images. -- After examining the computational complexity of the nonredundant contourlet transform, this thesis applies the NRCT to fingerprint image compression, since fingerprint images are examples of images with oriented structures. Based on an appropriately designed filter bank structure, the NRCT is easily compatible with the wavelet transform. Hence a new transform is created called the semi-NRCT, which takes the advantages of the directional selectivity of the NRCT and the lower complexity of the wavelet transform. Finally, this thesis proposes a new fingerprint image compression scheme based on the semi-NRCT. The semi-NRCT-based fingerprint image compression is compared with other transform-based compressions, for example the wavelet-based and the contourlet-based algorithms, and is shown to perform favorably

Distortion-constraint compression of three-dimensional CLSM images using image pyramid and vector quantization

The confocal microscopy imaging techniques, which allow optical sectioning, have been successfully exploited in biomedical studies. Biomedical scientists can benefit from more realistic visualization and much more accurate diagnosis by processing and analysing on a three-dimensional image data. The lack of efficient image compression standards makes such large volumetric image data slow to transfer over limited bandwidth networks. It also imposes large storage space requirements and high cost in archiving and maintenance. Conventional two-dimensional image coders do not take into account inter-frame correlations in three-dimensional image data. The standard multi-frame coders, like video coders, although they have good performance in capturing motion information, are not efficiently designed for coding multiple frames representing a stack of optical planes of a real object. Therefore a real three-dimensional image compression approach should be investigated. Moreover the reconstructed image quality is a very important concern in compressing medical images, because it could be directly related to the diagnosis accuracy. Most of the state-of-the-arts methods are based on transform coding, for instance JPEG is based on discrete-cosine-transform CDCT) and JPEG2000 is based on discrete- wavelet-transform (DWT). However in DCT and DWT methods, the control of the reconstructed image quality is inconvenient, involving considerable costs in computation, since they are fundamentally rate-parameterized methods rather than distortion-parameterized methods. Therefore it is very desirable to develop a transform-based distortion-parameterized compression method, which is expected to have high coding performance and also able to conveniently and accurately control the final distortion according to the user specified quality requirement. This thesis describes our work in developing a distortion-constraint three-dimensional image compression approach, using vector quantization techniques combined with image pyramid structures. We are expecting our method to have: 1. High coding performance in compressing three-dimensional microscopic image data, compared to the state-of-the-art three-dimensional image coders and other standardized two-dimensional image coders and video coders. 2. Distortion-control capability, which is a very desirable feature in medical 2. Distortion-control capability, which is a very desirable feature in medical image compression applications, is superior to the rate-parameterized methods in achieving a user specified quality requirement. The result is a three-dimensional image compression method, which has outstanding compression performance, measured objectively, for volumetric microscopic images. The distortion-constraint feature, by which users can expect to achieve a target image quality rather than the compressed file size, offers more flexible control of the reconstructed image quality than its rate-constraint counterparts in medical image applications. Additionally, it effectively reduces the artifacts presented in other approaches at low bit rates and also attenuates noise in the pre-compressed images. Furthermore, its advantages in progressive transmission and fast decoding make it suitable for bandwidth limited tele-communications and web-based image browsing applications

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Rate scalable image compression in the wavelet domain

This thesis explores image compression in the wavelet transform domain. This the- sis considers progressive compression based on bit plane coding. The rst part of the thesis investigates the scalar quantisation technique for multidimensional images such as colour and multispectral image. Embedded coders such as SPIHT and SPECK are known to be very simple and e cient algorithms for compression in the wavelet do- main. However, these algorithms require the use of lists to keep track of partitioning processes, and such lists involve high memory requirement during the encoding process. A listless approach has been proposed for multispectral image compression in order to reduce the working memory required. The earlier listless coders are extended into three dimensional coder so that redundancy in the spectral domain can be exploited. Listless implementation requires a xed memory of 4 bits per pixel to represent the state of each transformed coe cient. The state is updated during coding based on test of sig- ni cance. Spectral redundancies are exploited to improve the performance of the coder by modifying its scanning rules and the initial marker/state. For colour images, this is done by conducting a joint the signi cant test for the chrominance planes. In this way, the similarities between the chrominance planes can be exploited during the cod- ing process. Fixed memory listless methods that exploit spectral redundancies enable e cient coding while maintaining rate scalability and progressive transmission. The second part of the thesis addresses image compression using directional filters in the wavelet domain. A directional lter is expected to improve the retention of edge and curve information during compression. Current implementations of hybrid wavelet and directional (HWD) lters improve the contour representation of compressed images, but su er from the pseudo-Gibbs phenomenon in the smooth regions of the images. A di erent approach to directional lters in the wavelet transforms is proposed to remove such artifacts while maintaining the ability to preserve contours and texture. Imple- mentation with grayscale images shows improvements in terms of distortion rates and the structural similarity, especially in images with contours. The proposed transform manages to preserve the directional capability without pseudo-Gibbs artifacts and at the same time reduces the complexity of wavelet transform with directional lter. Fur-ther investigation to colour images shows the transform able to preserve texture and curve.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Représentation adaptative d'images de télédétection à très haute résolution spatiale une nouvelle approche hybride (la décomposition pyramidale avec des réseaux de neurones)

Résumé: De nos jours l’observation de la terre à l’aide d’images satellitaires de très haute résolution spatiale (Ikonos, Quickbird, World View-2) donne de nombreuses possibilités pour gérer de l’information à l’échelle mondiale. Les technologies actuelles d’acquisition d’information sont à l’origine de l’augmentation importante du volume des données. L’objectif général de cette thèse consiste à développer une nouvelle méthode hybride de représentation d’image numérique de très haute résolution spatiale qui améliore la qualité visuelle d’images compressée avec un haut niveau de compression (100 fois et plus). La nouvelle méthode hybride exploite la transformation pyramidale inverse d’image numérique en utilisant des réseaux de neurones artificiels. Elle combine le traitement spatial et la transformation abstraite de l’image. L’emploi de l’approche de la transformation pyramidale inverse a démontré l’efficacité du traitement de l’information à une ou à des échelles spécifiques, sans interférer ou ajouter un temps de calcul inutile. Cette approche est essentielle pour réaliser une transformation progressive d’image. Les résultats montrent une amélioration du rapport signal pur bruit de 4 dB pour chaque couche additionnelle de la transformation progressive. Nous avons réussi à garder une qualité visuelle d’images compressées comparable, jusqu’au niveau de la compression de 107 fois. De plus, pour le niveau de la compression de 274 fois, nous avons obtenu une amélioration de la qualité visuelle en comparaison des méthodes de compression courantes (JPEG, JPEG2000). Les résultats du travail confirment l’hypothèse que les images de télédétection possèdent un haut degré de redondance et que l’utilisation d’un réseau de neurones est un bon moyen pour trouver l’opérateur efficace du regroupement de pixels. Cette nouvelle méthode de représentation d’images à très haute résolution spatiale permet de réduire le volume des données sans détérioration majeure de la qualité visuelle, comparé aux méthodes existantes. Enfin, nous recommandons de poursuivre l’exploration du domaine des calculs distribués tels que les réseaux des neurones artificiels, considérant l’augmentation de la performance des outils informatiques (nanotechnologies et calculs parallèles). || Abstract: Earth observations using very high-resolution satellite imagery, such as from Ikonos, QuickBird or WorldView-2, provide many possibilities for addressing issues on a global scale. However, the acquisition of high-resolution imagery using these technologies also significantly increases the volume of data that must be managed. With the passing of each day, the number of collected satellite images continues to increase. The overall objective of this work is to develop new hybrid methods for numerical data representation that improve the visual quality of compressed satellite visible imagery for compression levels of 100 times and more. Our new method exploits the inverse pyramid transform using artificial neural networks, and thus addresses the trend in the field of remote sensing and image compression towards combining the spatial processing and abstract transformation of an image. Our implementation of the pyramidal inverse transformation demonstrates the effectiveness of information processing for specific levels, without interfering or adding unnecessary computation time. This approach is essential in order to achieve a gradual transformation of an image. The results showed an improvement in the signal to noise ratio of 4dB for each additional layer in the pyramidal image transformation. We managed to keep a similar level of visual quality for the compressed images up to a compression level of 107 times. In addition, for a compression level of 274, we improved the visual quality as compared to standard compression methods (JPEG, JPEG2000). The results of this study confirm the hypothesis that remote sensing images have a high degree of redundancy and that the use of neural networks is a good way to find the effective operator of the pixel combination. This new method for image representation reduces the volume of data without major deterioration in the visual quality of the compressed images, as compared to existing methods. Finally, we recommend further exploration in the field of distributed computing, such as artificial neural networks, considering the rapidly increasing performance of computers in the near future (parallel computing technology and nanotechnology)

Scalable video compression

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1992.Includes bibliographical references (leaves 85-88).by Joseph Bruce Stampleman.M.S

Wavelets and Subband Coding

First published in 1995, Wavelets and Subband Coding offered a unified view of the exciting field of wavelets and their discrete-time cousins, filter banks, or subband coding. The book developed the theory in both continuous and discrete time, and presented important applications. During the past decade, it filled a useful need in explaining a new view of signal processing based on flexible time-frequency analysis and its applications. Since 2007, the authors now retain the copyright and allow open access to the book