334 research outputs found
Energy Based Split Vector Quantizer Employing Signal Representation in Multiple Transform Domains.
This invention relates to representation of one and multidimensional signal vectors in nonorgothonal domains and design of Vector Quantizers that can be chosen among these representations. There is presented a Vector Quantization technique in multiple nonorthogonal domains for both waveform and model based signal characterization. An iterative codebook accuracy enhancement algorithm, applicable to both waveform and model based Vector Quantization in multiple nonorthogonal domains, which yields further improvement in signal coding performance, is disclosed. Further, Vector Quantization in in nonorthogonal domains is applied to speech and exhibits clear performance improvements of reconstruction quality for the same bit rate compared to existing single domain Vector Quantization techniques. The technique disclosed herein can be easily extended to several other one and multidimensional signal classes
Time and frequency domain algorithms for speech coding
The promise of digital hardware economies (due to recent advances in
VLSI technology), has focussed much attention on more complex and sophisticated
speech coding algorithms which offer improved quality at relatively
low bit rates.
This thesis describes the results (obtained from computer simulations)
of research into various efficient (time and frequency domain) speech
encoders operating at a transmission bit rate of 16 Kbps.
In the time domain, Adaptive Differential Pulse Code Modulation (ADPCM)
systems employing both forward and backward adaptive prediction were
examined. A number of algorithms were proposed and evaluated, including
several variants of the Stochastic Approximation Predictor (SAP). A
Backward Block Adaptive (BBA) predictor was also developed and found to
outperform the conventional stochastic methods, even though its complexity
in terms of signal processing requirements is lower. A simplified
Adaptive Predictive Coder (APC) employing a single tap pitch predictor
considered next provided a slight improvement in performance over ADPCM,
but with rather greater complexity.
The ultimate test of any speech coding system is the perceptual performance
of the received speech. Recent research has indicated that this
may be enhanced by suitable control of the noise spectrum according to
the theory of auditory masking. Various noise shaping ADPCM
configurations were examined, and it was demonstrated that a proposed
pre-/post-filtering arrangement which exploits advantageously the
predictor-quantizer interaction, leads to the best subjective
performance in both forward and backward prediction systems.
Adaptive quantization is instrumental to the performance of ADPCM systems.
Both the forward adaptive quantizer (AQF) and the backward oneword
memory adaptation (AQJ) were examined. In addition, a novel method
of decreasing quantization noise in ADPCM-AQJ coders, which involves the
application of correction to the decoded speech samples, provided
reduced output noise across the spectrum, with considerable high frequency
noise suppression.
More powerful (and inevitably more complex) frequency domain speech
coders such as the Adaptive Transform Coder (ATC) and the Sub-band Coder
(SBC) offer good quality speech at 16 Kbps. To reduce complexity and
coding delay, whilst retaining the advantage of sub-band coding, a novel
transform based split-band coder (TSBC) was developed and found to compare
closely in performance with the SBC.
To prevent the heavy side information requirement associated with a
large number of bands in split-band coding schemes from impairing coding
accuracy, without forgoing the efficiency provided by adaptive bit
allocation, a method employing AQJs to code the sub-band signals together
with vector quantization of the bit allocation patterns was also
proposed.
Finally, 'pipeline' methods of bit allocation and step size estimation
(using the Fast Fourier Transform (FFT) on the input signal) were examined.
Such methods, although less accurate, are nevertheless useful in
limiting coding delay associated with SRC schemes employing Quadrature
Mirror Filters (QMF)
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
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Speaker Identification Based On Discriminative Vector Quantization And Data Fusion
Speaker Identification (SI) approaches based on discriminative Vector Quantization (VQ) and data fusion techniques are presented in this dissertation. The SI approaches based on Discriminative VQ (DVQ) proposed in this dissertation are the DVQ for SI (DVQSI), the DVQSI with Unique speech feature vector space segmentation for each speaker pair (DVQSI-U), and the Adaptive DVQSI (ADVQSI) methods. The difference of the probability distributions of the speech feature vector sets from various speakers (or speaker groups) is called the interspeaker variation between speakers (or speaker groups). The interspeaker variation is the measure of template differences between speakers (or speaker groups). All DVQ based techniques presented in this contribution take advantage of the interspeaker variation, which are not exploited in the previous proposed techniques by others that employ traditional VQ for SI (VQSI). All DVQ based techniques have two modes, the training mode and the testing mode. In the training mode, the speech feature vector space is first divided into a number of subspaces based on the interspeaker variations. Then, a discriminative weight is calculated for each subspace of each speaker or speaker pair in the SI group based on the interspeaker variation. The subspaces with higher interspeaker variations play more important roles in SI than the ones with lower interspeaker variations by assigning larger discriminative weights. In the testing mode, discriminative weighted average VQ distortions instead of equally weighted average VQ distortions are used to make the SI decision. The DVQ based techniques lead to higher SI accuracies than VQSI. DVQSI and DVQSI-U techniques consider the interspeaker variation for each speaker pair in the SI group. In DVQSI, speech feature vector space segmentations for all the speaker pairs are exactly the same. However, each speaker pair of DVQSI-U is treated individually in the speech feature vector space segmentation. In both DVQSI and DVQSI-U, the discriminative weights for each speaker pair are calculated by trial and error. The SI accuracies of DVQSI-U are higher than those of DVQSI at the price of much higher computational burden. ADVQSI explores the interspeaker variation between each speaker and all speakers in the SI group. In contrast with DVQSI and DVQSI-U, in ADVQSI, the feature vector space segmentation is for each speaker instead of each speaker pair based on the interspeaker variation between each speaker and all the speakers in the SI group. Also, adaptive techniques are used in the discriminative weights computation for each speaker in ADVQSI. The SI accuracies employing ADVQSI and DVQSI-U are comparable. However, the computational complexity of ADVQSI is much less than that of DVQSI-U. Also, a novel algorithm to convert the raw distortion outputs of template-based SI classifiers into compatible probability measures is proposed in this dissertation. After this conversion, data fusion techniques at the measurement level can be applied to SI. In the proposed technique, stochastic models of the distortion outputs are estimated. Then, the posteriori probabilities of the unknown utterance belonging to each speaker are calculated. Compatible probability measures are assigned based on the posteriori probabilities. The proposed technique leads to better SI performance at the measurement level than existing approaches
Perceptual models in speech quality assessment and coding
The ever-increasing demand for good communications/toll
quality speech has created a renewed interest into the
perceptual impact of rate compression. Two general areas are
investigated in this work, namely speech quality assessment
and speech coding.
In the field of speech quality assessment, a model is
developed which simulates the processing stages of the
peripheral auditory system. At the output of the model a
"running" auditory spectrum is obtained. This represents
the auditory (spectral) equivalent of any acoustic sound such
as speech. Auditory spectra from coded speech segments serve
as inputs to a second model. This model simulates the
information centre in the brain which performs the speech
quality assessment. [Continues.
REGION-BASED ADAPTIVE DISTRIBUTED VIDEO CODING CODEC
The recently developed Distributed Video Coding (DVC) is typically suitable for the
applications where the conventional video coding is not feasible because of its
inherent high-complexity encoding. Examples include video surveillance usmg
wireless/wired video sensor network and applications using mobile cameras etc. With
DVC, the complexity is shifted from the encoder to the decoder.
The practical application of DVC is referred to as Wyner-Ziv video coding (WZ)
where an estimate of the original frame called "side information" is generated using
motion compensation at the decoder. The compression is achieved by sending only
that extra information that is needed to correct this estimation. An error-correcting
code is used with the assumption that the estimate is a noisy version of the original
frame and the rate needed is certain amount of the parity bits. The side information is
assumed to have become available at the decoder through a virtual channel. Due to
the limitation of compensation method, the predicted frame, or the side information, is
expected to have varying degrees of success. These limitations stem from locationspecific
non-stationary estimation noise. In order to avoid these, the conventional
video coders, like MPEG, make use of frame partitioning to allocate optimum coder
for each partition and hence achieve better rate-distortion performance. The same,
however, has not been used in DVC as it increases the encoder complexity.
This work proposes partitioning the considered frame into many coding units
(region) where each unit is encoded differently. This partitioning is, however, done at
the decoder while generating the side-information and the region map is sent over to
encoder at very little rate penalty. The partitioning allows allocation of appropriate
DVC coding parameters (virtual channel, rate, and quantizer) to each region. The
resulting regions map is compressed by employing quadtree algorithm and
communicated to the encoder via the feedback channel. The rate control in DVC is
performed by channel coding techniques (turbo codes, LDPC, etc.). The performance
of the channel code depends heavily on the accuracy of virtual channel model that models estimation error for each region. In this work, a turbo code has been used and
an adaptive WZ DVC is designed both in transform domain and in pixel domain. The
transform domain WZ video coding (TDWZ) has distinct superior performance as
compared to the normal Pixel Domain Wyner-Ziv (PDWZ), since it exploits the
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spatial redundancy during the encoding. The performance evaluations show that the
proposed system is superior to the existing distributed video coding solutions.
Although the, proposed system requires extra bits representing the "regions map" to be
transmitted, fuut still the rate gain is noticeable and it outperforms the state-of-the-art
frame based DVC by 0.6-1.9 dB.
The feedback channel (FC) has the role to adapt the bit rate to the changing
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statistics between the side infonmation and the frame to be encoded. In the
unidirectional scenario, the encoder must perform the rate control. To correctly
estimate the rate, the encoder must calculate typical side information. However, the
rate cannot be exactly calculated at the encoder, instead it can only be estimated. This
work also prbposes a feedback-free region-based adaptive DVC solution in pixel
domain based on machine learning approach to estimate the side information.
Although the performance evaluations show rate-penalty but it is acceptable
considering the simplicity of the proposed algorithm.
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