Fractal image compression and the self-affinity assumption : a stochastic signal modelling perspective

Bibliography: p. 208-225.Fractal image compression is a comparatively new technique which has gained considerable attention in the popular technical press, and more recently in the research literature. The most significant advantages claimed are high reconstruction quality at low coding rates, rapid decoding, and "resolution independence" in the sense that an encoded image may be decoded at a higher resolution than the original. While many of the claims published in the popular technical press are clearly extravagant, it appears from the rapidly growing body of published research that fractal image compression is capable of performance comparable with that of other techniques enjoying the benefit of a considerably more robust theoretical foundation. . So called because of the similarities between the form of image representation and a mechanism widely used in generating deterministic fractal images, fractal compression represents an image by the parameters of a set of affine transforms on image blocks under which the image is approximately invariant. Although the conditions imposed on these transforms may be shown to be sufficient to guarantee that an approximation of the original image can be reconstructed, there is no obvious theoretical reason to expect this to represent an efficient representation for image coding purposes. The usual analogy with vector quantisation, in which each image is considered to be represented in terms of code vectors extracted from the image itself is instructive, but transforms the fundamental problem into one of understanding why this construction results in an efficient codebook. The signal property required for such a codebook to be effective, termed "self-affinity", is poorly understood. A stochastic signal model based examination of this property is the primary contribution of this dissertation. The most significant findings (subject to some important restrictions} are that "self-affinity" is not a natural consequence of common statistical assumptions but requires particular conditions which are inadequately characterised by second order statistics, and that "natural" images are only marginally "self-affine", to the extent that fractal image compression is effective, but not more so than comparable standard vector quantisation techniques

Significant medical image compression techniques: a review

Telemedicine applications allow the patient and doctor to communicate with each other through network services. Several medical image compression techniques have been suggested by researchers in the past years. This review paper offers a comparison of the algorithms and the performance by analysing three factors that influence the choice of compression algorithm, which are image quality, compression ratio, and compression speed. The results of previous research have shown that there is a need for effective algorithms for medical imaging without data loss, which is why the lossless compression process is used to compress medical records. Lossless compression, however, has minimal compression ratio efficiency. The way to get the optimum compression ratio is by segmentation of the image into region of interest (ROI) and non-ROI zones, where the power and time needed can be minimised due to the smaller scale. Recently, several researchers have been attempting to create hybrid compression algorithms by integrating different compression techniques to increase the efficiency of compression algorithms

Parallel implementation of fractal image compression

Thesis (M.Sc.Eng.)-University of Natal, Durban, 2000.Fractal image compression exploits the piecewise self-similarity present in real images as a form of information redundancy that can be eliminated to achieve compression. This theory based on Partitioned Iterated Function Systems is presented. As an alternative to the established JPEG, it provides a similar compression-ratio to fidelity trade-off. Fractal techniques promise faster decoding and potentially higher fidelity, but the computationally intensive compression process has prevented commercial acceptance. This thesis presents an algorithm mapping the problem onto a parallel processor architecture, with the goal of reducing the encoding time. The experimental work involved implementation of this approach on the Texas Instruments TMS320C80 parallel processor system. Results indicate that the fractal compression process is unusually well suited to parallelism with speed gains approximately linearly related to the number of processors used. Parallel processing issues such as coherency, management and interfacing are discussed. The code designed incorporates pipelining and parallelism on all conceptual and practical levels ensuring that all resources are fully utilised, achieving close to optimal efficiency. The computational intensity was reduced by several means, including conventional classification of image sub-blocks by content with comparisons across class boundaries prohibited. A faster approach adopted was to perform estimate comparisons between blocks based on pixel value variance, identifying candidates for more time-consuming, accurate RMS inter-block comparisons. These techniques, combined with the parallelism, allow compression of 512x512 pixel x 8 bit images in under 20 seconds, while maintaining a 30dB PSNR. This is up to an order of magnitude faster than reported for conventional sequential processor implementations. Fractal based compression of colour images and video sequences is also considered. The work confirms the potential of fractal compression techniques, and demonstrates that a parallel implementation is appropriate for addressing the compression time problem. The processor system used in these investigations is faster than currently available PC platforms, but the relevance lies in the anticipation that future generations of affordable processors will exceed its performance. The advantages of fractal image compression may then be accessible to the average computer user, leading to commercial acceptance

Proceedings of the Scientific Data Compression Workshop

Continuing advances in space and Earth science requires increasing amounts of data to be gathered from spaceborne sensors. NASA expects to launch sensors during the next two decades which will be capable of producing an aggregate of 1500 Megabits per second if operated simultaneously. Such high data rates cause stresses in all aspects of end-to-end data systems. Technologies and techniques are needed to relieve such stresses. Potential solutions to the massive data rate problems are: data editing, greater transmission bandwidths, higher density and faster media, and data compression. Through four subpanels on Science Payload Operations, Multispectral Imaging, Microwave Remote Sensing and Science Data Management, recommendations were made for research in data compression and scientific data applications to space platforms

Parallel implementation of fractal image compression

Thesis (M.Sc.Eng.)-University of Natal, Durban, 2000.Fractal image compression exploits the piecewise self-similarity present in real images as a form of information redundancy that can be eliminated to achieve compression. This theory based on Partitioned Iterated Function Systems is presented. As an alternative to the established JPEG, it provides a similar compression-ratio to fidelity trade-off. Fractal techniques promise faster decoding and potentially higher fidelity, but the computationally intensive compression process has prevented commercial acceptance. This thesis presents an algorithm mapping the problem onto a parallel processor architecture, with the goal of reducing the encoding time. The experimental work involved implementation of this approach on the Texas Instruments TMS320C80 parallel processor system. Results indicate that the fractal compression process is unusually well suited to parallelism with speed gains approximately linearly related to the number of processors used. Parallel processing issues such as coherency, management and interfacing are discussed. The code designed incorporates pipelining and parallelism on all conceptual and practical levels ensuring that all resources are fully utilised, achieving close to optimal efficiency. The computational intensity was reduced by several means, including conventional classification of image sub-blocks by content with comparisons across class boundaries prohibited. A faster approach adopted was to perform estimate comparisons between blocks based on pixel value variance, identifying candidates for more time-consuming, accurate RMS inter-block comparisons. These techniques, combined with the parallelism, allow compression of 512x512 pixel x 8 bit images in under 20 seconds, while maintaining a 30dB PSNR. This is up to an order of magnitude faster than reported for conventional sequential processor implementations. Fractal based compression of colour images and video sequences is also considered. The work confirms the potential of fractal compression techniques, and demonstrates that a parallel implementation is appropriate for addressing the compression time problem. The processor system used in these investigations is faster than currently available PC platforms, but the relevance lies in the anticipation that future generations of affordable processors will exceed its performance. The advantages of fractal image compression may then be accessible to the average computer user, leading to commercial acceptance

Development of Some Efficient Lossless and Lossy Hybrid Image Compression Schemes

Digital imaging generates a large amount of data which needs to be compressed, without loss of relevant information, to economize storage space and allow speedy data transfer. Though both storage and transmission medium capacities have been continuously increasing over the last two decades, they dont match the present requirement. Many lossless and lossy image compression schemes exist for compression of images in space domain and transform domain. Employing more than one traditional image compression algorithms results in hybrid image compression techniques. Based on the existing schemes, novel hybrid image compression schemes are developed in this doctoral research work, to compress the images effectually maintaining the quality

Compression et transmission d'images avec énergie minimale application aux capteurs sans fil

Un réseau de capteurs d'images sans fil (RCISF) est un réseau ad hoc formé d'un ensemble de noeuds autonomes dotés chacun d'une petite caméra, communiquant entre eux sans liaison filaire et sans l'utilisation d'une infrastructure établie, ni d'une gestion de réseau centralisée. Leur utilité semble majeure dans plusieurs domaines, notamment en médecine et en environnement. La conception d'une chaîne de compression et de transmission sans fil pour un RCISF pose de véritables défis. L'origine de ces derniers est liée principalement à la limitation des ressources des capteurs (batterie faible , capacité de traitement et mémoire limitées). L'objectif de cette thèse consiste à explorer des stratégies permettant d'améliorer l'efficacité énergétique des RCISF, notamment lors de la compression et de la transmission des images. Inéluctablement, l'application des normes usuelles telles que JPEG ou JPEG2000 est éner- givore, et limite ainsi la longévité des RCISF. Cela nécessite leur adaptation aux contraintes imposées par les RCISF. Pour cela, nous avons analysé en premier lieu, la faisabilité d'adapter JPEG au contexte où les ressources énergétiques sont très limitées. Les travaux menés sur cet aspect nous permettent de proposer trois solutions. La première solution est basée sur la propriété de compactage de l'énergie de la Transformée en Cosinus Discrète (TCD). Cette propriété permet d'éliminer la redondance dans une image sans trop altérer sa qualité, tout en gagnant en énergie. La réduction de l'énergie par l'utilisation des régions d'intérêts représente la deuxième solution explorée dans cette thèse. Finalement, nous avons proposé un schéma basé sur la compression et la transmission progressive, permettant ainsi d'avoir une idée générale sur l'image cible sans envoyer son contenu entier. En outre, pour une transmission non énergivore, nous avons opté pour la solution suivante. N'envoyer fiablement que les basses fréquences et les régions d'intérêt d'une image. Les hautes fréquences et les régions de moindre intérêt sont envoyées""infiablement"", car leur pertes n'altèrent que légèrement la qualité de l'image. Pour cela, des modèles de priorisation ont été comparés puis adaptés à nos besoins. En second lieu, nous avons étudié l'approche par ondelettes (wavelets ). Plus précisément, nous avons analysé plusieurs filtres d'ondelettes et déterminé les ondelettes les plus adéquates pour assurer une faible consommation en énergie, tout en gardant une bonne qualité de l'image reconstruite à la station de base. Pour estimer l'énergie consommée par un capteur durant chaque étape de la 'compression, un modèle mathématique est développé pour chaque transformée (TCD ou ondelette). Ces modèles, qui ne tiennent pas compte de la complexité de l'implémentation, sont basés sur le nombre d'opérations de base exécutées à chaque étape de la compression