397 research outputs found
Audio Coding Based on Integer Transforms
Die Audiocodierung hat sich in den letzten Jahren zu einem sehr
populären Forschungs- und Anwendungsgebiet entwickelt. Insbesondere
gehörangepasste Verfahren zur Audiocodierung, wie etwa MPEG-1 Layer-3
(MP3) oder MPEG-2 Advanced Audio Coding (AAC), werden häufig zur
effizienten Speicherung und Ăśbertragung von Audiosignalen verwendet. FĂĽr
professionelle Anwendungen, wie etwa die Archivierung und Ăśbertragung im
Studiobereich, ist hingegen eher eine verlustlose Audiocodierung angebracht.
Die bisherigen Ansätze für gehörangepasste und verlustlose
Audiocodierung sind technisch völlig verschieden. Moderne
gehörangepasste Audiocoder basieren meist auf Filterbänken, wie etwa der
ĂĽberlappenden orthogonalen Transformation "Modifizierte Diskrete
Cosinus-Transformation" (MDCT). Verlustlose Audiocoder hingegen
verwenden meist prädiktive Codierung zur Redundanzreduktion. Nur wenige
Ansätze zur transformationsbasierten verlustlosen Audiocodierung wurden
bisher versucht.
Diese Arbeit präsentiert einen neuen Ansatz hierzu, der das
Lifting-Schema auf die in der gehörangepassten Audiocodierung
verwendeten überlappenden Transformationen anwendet. Dies ermöglicht
eine invertierbare Integer-Approximation der ursprĂĽnglichen
Transformation, z.B. die IntMDCT als Integer-Approximation der MDCT. Die
selbe Technik kann auch für Filterbänke mit niedriger Systemverzögerung
angewandt werden. Weiterhin ermöglichen ein neuer, mehrdimensionaler
Lifting-Ansatz und eine Technik zur Spektralformung von
Quantisierungsfehlern eine Verbesserung der Approximation der
ursprĂĽnglichen Transformation.
Basierend auf diesen neuen Integer-Transformationen werden in dieser
Arbeit neue Verfahren zur Audiocodierung vorgestellt. Die Verfahren
umfassen verlustlose Audiocodierung, eine skalierbare verlustlose
Erweiterung eines gehörangepassten Audiocoders und einen integrierten
Ansatz zur fein skalierbaren gehörangepassten und verlustlosen
Audiocodierung. SchlieĂźlich wird mit Hilfe der Integer-Transformationen
ein neuer Ansatz zur unhörbaren Einbettung von Daten mit hohen
Datenraten in unkomprimierte Audiosignale vorgestellt.In recent years audio coding has become a very popular field for
research and applications. Especially perceptual audio coding schemes,
such as MPEG-1 Layer-3 (MP3) and MPEG-2 Advanced Audio Coding (AAC), are
widely used for efficient storage and transmission of music
signals. Nevertheless, for professional applications, such as archiving
and transmission in studio environments, lossless audio coding schemes
are considered more appropriate.
Traditionally, the technical approaches used in perceptual and lossless
audio coding have been separate worlds. In perceptual audio coding, the
use of filter banks, such as the lapped orthogonal transform "Modified
Discrete Cosine Transform" (MDCT), has been the approach of choice being
used by many state of the art coding schemes. On the other hand,
lossless audio coding schemes mostly employ predictive coding of
waveforms to remove redundancy. Only few attempts have been made so far
to use transform coding for the purpose of lossless audio coding.
This work presents a new approach of applying the lifting scheme to
lapped transforms used in perceptual audio coding. This allows for an
invertible integer-to-integer approximation of the original transform,
e.g. the IntMDCT as an integer approximation of the MDCT. The same
technique can also be applied to low-delay filter banks. A generalized,
multi-dimensional lifting approach and a noise-shaping technique are
introduced, allowing to further optimize the accuracy of the
approximation to the original transform.
Based on these new integer transforms, this work presents new audio
coding schemes and applications. The audio coding applications cover
lossless audio coding, scalable lossless enhancement of a perceptual
audio coder and fine-grain scalable perceptual and lossless audio
coding. Finally an approach to data hiding with high data rates in
uncompressed audio signals based on integer transforms is described
On the design of fast and efficient wavelet image coders with reduced memory usage
Image compression is of great importance in multimedia systems and
applications because it drastically reduces bandwidth requirements for
transmission and memory requirements for storage. Although earlier
standards for image compression were based on the Discrete Cosine
Transform (DCT), a recently developed mathematical technique, called
Discrete Wavelet Transform (DWT), has been found to be more efficient
for image coding.
Despite improvements in compression efficiency, wavelet image coders
significantly increase memory usage and complexity when compared with
DCT-based coders. A major reason for the high memory requirements is
that the usual algorithm to compute the wavelet transform requires the
entire image to be in memory. Although some proposals reduce the memory
usage, they present problems that hinder their implementation. In
addition, some wavelet image coders, like SPIHT (which has become a
benchmark for wavelet coding), always need to hold the entire image in
memory.
Regarding the complexity of the coders, SPIHT can be considered quite
complex because it performs bit-plane coding with multiple image scans.
The wavelet-based JPEG 2000 standard is still more complex because it
improves coding efficiency through time-consuming methods, such as an
iterative optimization algorithm based on the Lagrange multiplier
method, and high-order context modeling.
In this thesis, we aim to reduce memory usage and complexity in
wavelet-based image coding, while preserving compression efficiency. To
this end, a run-length encoder and a tree-based wavelet encoder are
proposed. In addition, a new algorithm to efficiently compute the
wavelet transform is presented. This algorithm achieves low memory
consumption using line-by-line processing, and it employs recursion to
automatically place the order in which the wavelet transform is
computed, solving some synchronization problems that have not been
tackled by previous proposals. The proposed encodeOliver Gil, JS. (2006). On the design of fast and efficient wavelet image coders with reduced memory usage [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1826Palanci
Geophysical imaging using trans-dimensional trees.
In geophysical inversion, inferences of Earth's properties from sparse data involve a trade-off between model complexity and the spatial resolving power. A recent Markov chain Monte Carlo (McMC) technique formalized by Green, the so-called trans-dimensional samplers, allows us to sample between these trade-offs and to parsimoniously arbitrate between the varying complexity of candidate models. Here we present a novel framework using trans-dimensional sampling over tree structures. This new class of McMC sampler can be applied to 1-D, 2-D and 3-D Cartesian and spherical geometries. In addition, the basis functions used by the algorithm are flexible and can include more advanced parametrizations such as wavelets, both in Cartesian and Spherical geometries, to permit Bayesian multiscale analysis. This new framework offers greater flexibility, performance and efficiency for geophysical imaging problems than previous sampling algorithms. Thereby increasing the range of applications and in particular allowing extension to trans-dimensional imaging in 3-D. Examples are presented of its application to 2-D seismic and 3-D teleseismic tomography including estimation of uncertainty
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