1,048 research outputs found
Self-similarity and wavelet forms for the compression of still image and video data
This thesis is concerned with the methods used to reduce the data volume required to represent
still images and video sequences. The number of disparate still image and video
coding methods increases almost daily. Recently, two new strategies have emerged and
have stimulated widespread research. These are the fractal method and the wavelet transform.
In this thesis, it will be argued that the two methods share a common principle: that
of self-similarity. The two will be related concretely via an image coding algorithm which
combines the two, normally disparate, strategies.
The wavelet transform is an orientation selective transform. It will be shown that the
selectivity of the conventional transform is not sufficient to allow exploitation of self-similarity
while keeping computational cost low. To address this, a new wavelet transform
is presented which allows for greater orientation selectivity, while maintaining the
orthogonality and data volume of the conventional wavelet transform. Many designs for
vector quantizers have been published recently and another is added to the gamut by this
work. The tree structured vector quantizer presented here is on-line and self structuring,
requiring no distinct training phase. Combining these into a still image data compression
system produces results which are among the best that have been published to date.
An extension of the two dimensional wavelet transform to encompass the time dimension
is straightforward and this work attempts to extrapolate some of its properties into three
dimensions. The vector quantizer is then applied to three dimensional image data to
produce a video coding system which, while not optimal, produces very encouraging
results
Formal Representation of the SS-DB Benchmark and Experimental Evaluation in EXTASCID
Evaluating the performance of scientific data processing systems is a
difficult task considering the plethora of application-specific solutions
available in this landscape and the lack of a generally-accepted benchmark. The
dual structure of scientific data coupled with the complex nature of processing
complicate the evaluation procedure further. SS-DB is the first attempt to
define a general benchmark for complex scientific processing over raw and
derived data. It fails to draw sufficient attention though because of the
ambiguous plain language specification and the extraordinary SciDB results. In
this paper, we remedy the shortcomings of the original SS-DB specification by
providing a formal representation in terms of ArrayQL algebra operators and
ArrayQL/SciQL constructs. These are the first formal representations of the
SS-DB benchmark. Starting from the formal representation, we give a reference
implementation and present benchmark results in EXTASCID, a novel system for
scientific data processing. EXTASCID is complete in providing native support
both for array and relational data and extensible in executing any user code
inside the system by the means of a configurable metaoperator. These features
result in an order of magnitude improvement over SciDB at data loading,
extracting derived data, and operations over derived data.Comment: 32 pages, 3 figure
Giving eyes to ICT!, or How does a computer recognize a cow?
Het door Schouten en andere onderzoekers op het CWI ontwikkelde systeem berust op het beschrijven van beelden met behulp van fractale meetkunde. De menselijke waarneming blijkt mede daardoor zo efficiënt omdat zij sterk werkt met gelijkenissen. Het ligt dus voor de hand het te zoeken in wiskundige methoden die dat ook doen. Schouten heeft daarom beeldcodering met behulp van 'fractals' onderzocht. Fractals zijn zelfgelijkende meetkundige figuren, opgebouwd door herhaalde transformatie (iteratie) van een eenvoudig basispatroon, dat zich daardoor op steeds kleinere schalen vertakt. Op elk niveau van detaillering lijkt een fractal op zichzelf (Droste-effect). Met fractals kan men vrij eenvoudig bedrieglijk echte natuurvoorstellingen maken. Fractale beeldcodering gaat ervan uit dat het omgekeerde ook geldt: een beeld effectief opslaan in de vorm van de basispatronen van een klein aantal fractals, samen met het voorschrift hoe het oorspronkelijke beeld daaruit te reconstrueren. Het op het CWI in samenwerking met onderzoekers uit Leuven ontwikkelde systeem is mede gebaseerd op deze methode.
ISBN 906196502
Composite Correlation Quantization for Efficient Multimodal Retrieval
Efficient similarity retrieval from large-scale multimodal database is
pervasive in modern search engines and social networks. To support queries
across content modalities, the system should enable cross-modal correlation and
computation-efficient indexing. While hashing methods have shown great
potential in achieving this goal, current attempts generally fail to learn
isomorphic hash codes in a seamless scheme, that is, they embed multiple
modalities in a continuous isomorphic space and separately threshold embeddings
into binary codes, which incurs substantial loss of retrieval accuracy. In this
paper, we approach seamless multimodal hashing by proposing a novel Composite
Correlation Quantization (CCQ) model. Specifically, CCQ jointly finds
correlation-maximal mappings that transform different modalities into
isomorphic latent space, and learns composite quantizers that convert the
isomorphic latent features into compact binary codes. An optimization framework
is devised to preserve both intra-modal similarity and inter-modal correlation
through minimizing both reconstruction and quantization errors, which can be
trained from both paired and partially paired data in linear time. A
comprehensive set of experiments clearly show the superior effectiveness and
efficiency of CCQ against the state of the art hashing methods for both
unimodal and cross-modal retrieval
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
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
Efficient Parallel Processing of k-Nearest Neighbor Queries by Using a Centroid-based and Hierarchical Clustering Algorithm
The k-Nearest Neighbor method is one of the most popular techniques for both classification and regression purposes. Because of its operation, the application of this classification may be limited to problems with a certain number of instances, particularly, when run time is a consideration. However, the classification of large amounts of data has become a fundamental task in many real-world applications. It is logical to scale the k-Nearest Neighbor method to large scale datasets. This paper proposes a new k-Nearest Neighbor classification method (KNN-CCL) which uses a parallel centroid-based and hierarchical clustering algorithm to separate the sample of training dataset into multiple parts. The introduced clustering algorithm uses four stages of successive refinements and generates high quality clusters. The k-Nearest Neighbor approach subsequently makes use of them to predict the test datasets. Finally, sets of experiments are conducted on the UCI datasets. The experimental results confirm that the proposed k-Nearest Neighbor classification method performs well with regard to classification accuracy and performance
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