1,292 research outputs found
Relaxation Time of Quantized Toral Maps
We introduce the notion of the relaxation time for noisy quantum maps on the
2d-dimensional torus - a generalization of previously studied dissipation time.
We show that relaxation time is sensitive to the chaotic behavior of the
corresponding classical system if one simultaneously considers the
semiclassical limit ( -> 0) together with the limit of small noise
strength (\ep -> 0).
Focusing on quantized smooth Anosov maps, we exhibit a semiclassical regime
\hbar^{-1}\ep\hbar$ << 1,
quantum and classical relaxation times behave very differently. In the special
case of ergodic toral symplectomorphisms (generalized ``Arnold's cat'' maps),
we obtain the exact asymptotics of the quantum relaxation time and precise the
regime of correspondence between quantum and classical relaxations.Comment: LaTeX, 27 pages, former term dissipation time replaced by relaxation
time, new introduction and reference
Hashing for Similarity Search: A Survey
Similarity search (nearest neighbor search) is a problem of pursuing the data
items whose distances to a query item are the smallest from a large database.
Various methods have been developed to address this problem, and recently a lot
of efforts have been devoted to approximate search. In this paper, we present a
survey on one of the main solutions, hashing, which has been widely studied
since the pioneering work locality sensitive hashing. We divide the hashing
algorithms two main categories: locality sensitive hashing, which designs hash
functions without exploring the data distribution and learning to hash, which
learns hash functions according the data distribution, and review them from
various aspects, including hash function design and distance measure and search
scheme in the hash coding space
Audio Coding Based on Integer Transforms
Die Audiocodierung hat sich in den letzten Jahren zu einem sehr
populÀren Forschungs- und Anwendungsgebiet entwickelt. Insbesondere
gehörangepasste Verfahren zur Audiocodierung, wie etwa MPEG-1 Layer-3
(MP3) oder MPEG-2 Advanced Audio Coding (AAC), werden hÀufig zur
effizienten Speicherung und Ăbertragung von Audiosignalen verwendet. FĂŒr
professionelle Anwendungen, wie etwa die Archivierung und Ăbertragung im
Studiobereich, ist hingegen eher eine verlustlose Audiocodierung angebracht.
Die bisherigen AnsĂ€tze fĂŒr gehörangepasste und verlustlose
Audiocodierung sind technisch völlig verschieden. Moderne
gehörangepasste Audiocoder basieren meist auf FilterbÀnken, wie etwa der
ĂŒberlappenden orthogonalen Transformation "Modifizierte Diskrete
Cosinus-Transformation" (MDCT). Verlustlose Audiocoder hingegen
verwenden meist prÀdiktive Codierung zur Redundanzreduktion. Nur wenige
AnsÀtze zur transformationsbasierten verlustlosen Audiocodierung wurden
bisher versucht.
Diese Arbeit prÀsentiert einen neuen Ansatz hierzu, der das
Lifting-Schema auf die in der gehörangepassten Audiocodierung
verwendeten ĂŒberlappenden Transformationen anwendet. Dies ermöglicht
eine invertierbare Integer-Approximation der ursprĂŒnglichen
Transformation, z.B. die IntMDCT als Integer-Approximation der MDCT. Die
selbe Technik kann auch fĂŒr FilterbĂ€nke mit niedriger Systemverzögerung
angewandt werden. Weiterhin ermöglichen ein neuer, mehrdimensionaler
Lifting-Ansatz und eine Technik zur Spektralformung von
Quantisierungsfehlern eine Verbesserung der Approximation der
ursprĂŒnglichen 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. SchlieĂlich 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
Audio Coding Based on Integer Transforms
Die Audiocodierung hat sich in den letzten Jahren zu einem sehr
populÀren Forschungs- und Anwendungsgebiet entwickelt. Insbesondere
gehörangepasste Verfahren zur Audiocodierung, wie etwa MPEG-1 Layer-3
(MP3) oder MPEG-2 Advanced Audio Coding (AAC), werden hÀufig zur
effizienten Speicherung und Ăbertragung von Audiosignalen verwendet. FĂŒr
professionelle Anwendungen, wie etwa die Archivierung und Ăbertragung im
Studiobereich, ist hingegen eher eine verlustlose Audiocodierung angebracht.
Die bisherigen AnsĂ€tze fĂŒr gehörangepasste und verlustlose
Audiocodierung sind technisch völlig verschieden. Moderne
gehörangepasste Audiocoder basieren meist auf FilterbÀnken, wie etwa der
ĂŒberlappenden orthogonalen Transformation "Modifizierte Diskrete
Cosinus-Transformation" (MDCT). Verlustlose Audiocoder hingegen
verwenden meist prÀdiktive Codierung zur Redundanzreduktion. Nur wenige
AnsÀtze zur transformationsbasierten verlustlosen Audiocodierung wurden
bisher versucht.
Diese Arbeit prÀsentiert einen neuen Ansatz hierzu, der das
Lifting-Schema auf die in der gehörangepassten Audiocodierung
verwendeten ĂŒberlappenden Transformationen anwendet. Dies ermöglicht
eine invertierbare Integer-Approximation der ursprĂŒnglichen
Transformation, z.B. die IntMDCT als Integer-Approximation der MDCT. Die
selbe Technik kann auch fĂŒr FilterbĂ€nke mit niedriger Systemverzögerung
angewandt werden. Weiterhin ermöglichen ein neuer, mehrdimensionaler
Lifting-Ansatz und eine Technik zur Spektralformung von
Quantisierungsfehlern eine Verbesserung der Approximation der
ursprĂŒnglichen 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. SchlieĂlich 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
Data Hiding in Digital Video
With the rapid development of digital multimedia technologies, an old method which is called steganography has been sought to be a solution for data hiding applications such as digital watermarking and covert communication. Steganography is the art of secret communication using a cover signal, e.g., video, audio, image etc., whereas the counter-technique, detecting the existence of such as a channel through a statistically trained classifier, is called steganalysis.
The state-of-the art data hiding algorithms utilize features; such as Discrete Cosine Transform (DCT) coefficients, pixel values, motion vectors etc., of the cover signal to convey the message to the receiver side. The goal of embedding algorithm is to maximize the number of bits sent to the decoder side (embedding capacity) with maximum robustness against attacks while keeping the perceptual and statistical distortions (security) low. Data Hiding schemes are characterized by these three conflicting requirements: security against steganalysis, robustness against channel associated and/or intentional distortions, and the capacity in terms of the embedded payload. Depending upon the application it is the designer\u27s task to find an optimum solution amongst them.
The goal of this thesis is to develop a novel data hiding scheme to establish a covert channel satisfying statistical and perceptual invisibility with moderate rate capacity and robustness to combat steganalysis based detection. The idea behind the proposed method is the alteration of Video Object (VO) trajectory coordinates to convey the message to the receiver side by perturbing the centroid coordinates of the VO. Firstly, the VO is selected by the user and tracked through the frames by using a simple region based search strategy and morphological operations. After the trajectory coordinates are obtained, the perturbation of the coordinates implemented through the usage of a non-linear embedding function, such as a polar quantizer where both the magnitude and phase of the motion is used. However, the perturbations made to the motion magnitude and phase were kept small to preserve the semantic meaning of the object motion trajectory.
The proposed method is well suited to the video sequences in which VOs have smooth motion trajectories. Examples of these types could be found in sports videos in which the ball is the focus of attention and exhibits various motion types, e.g., rolling on the ground, flying in the air, being possessed by a player, etc. Different sports video sequences have been tested by using the proposed method. Through the experimental results, it is shown that the proposed method achieved the goal of both statistical and perceptual invisibility with moderate rate embedding capacity under AWGN channel with varying noise variances. This achievement is important as the first step for both active and passive steganalysis is the detection of the existence of covert channel.
This work has multiple contributions in the field of data hiding. Firstly, it is the first example of a data hiding method in which the trajectory of a VO is used. Secondly, this work has contributed towards improving steganographic security by providing new features: the coordinate location and semantic meaning of the object
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