2,999 research outputs found
Improving Low Bit-Rate Video Coding using Spatio-Temporal Down-Scaling
Good quality video coding for low bit-rate applications is important for
transmission over narrow-bandwidth channels and for storage with limited memory
capacity. In this work, we develop a previous analysis for image compression at
low bit-rates to adapt it to video signals. Improving compression using
down-scaling in the spatial and temporal dimensions is examined. We show, both
theoretically and experimentally, that at low bit-rates, we benefit from
applying spatio-temporal scaling. The proposed method includes down-scaling
before the compression and a corresponding up-scaling afterwards, while the
codec itself is left unmodified. We propose analytic models for low bit-rate
compression and spatio-temporal scaling operations. Specifically, we use
theoretic models of motion-compensated prediction of available and absent
frames as in coding and frame-rate up-conversion (FRUC) applications,
respectively. The proposed models are designed for multi-resolution analysis.
In addition, we formulate a bit-allocation procedure and propose a method for
estimating good down-scaling factors of a given video based on its second-order
statistics and the given bit-budget. We validate our model with experimental
results of H.264 compression
Rate-Distortion Analysis of Multiview Coding in a DIBR Framework
Depth image based rendering techniques for multiview applications have been
recently introduced for efficient view generation at arbitrary camera
positions. Encoding rate control has thus to consider both texture and depth
data. Due to different structures of depth and texture images and their
different roles on the rendered views, distributing the available bit budget
between them however requires a careful analysis. Information loss due to
texture coding affects the value of pixels in synthesized views while errors in
depth information lead to shift in objects or unexpected patterns at their
boundaries. In this paper, we address the problem of efficient bit allocation
between textures and depth data of multiview video sequences. We adopt a
rate-distortion framework based on a simplified model of depth and texture
images. Our model preserves the main features of depth and texture images.
Unlike most recent solutions, our method permits to avoid rendering at encoding
time for distortion estimation so that the encoding complexity is not
augmented. In addition to this, our model is independent of the underlying
inpainting method that is used at decoder. Experiments confirm our theoretical
results and the efficiency of our rate allocation strategy
Application of Compressive Sensing Techniques in Distributed Sensor Networks: A Survey
In this survey paper, our goal is to discuss recent advances of compressive
sensing (CS) based solutions in wireless sensor networks (WSNs) including the
main ongoing/recent research efforts, challenges and research trends in this
area. In WSNs, CS based techniques are well motivated by not only the sparsity
prior observed in different forms but also by the requirement of efficient
in-network processing in terms of transmit power and communication bandwidth
even with nonsparse signals. In order to apply CS in a variety of WSN
applications efficiently, there are several factors to be considered beyond the
standard CS framework. We start the discussion with a brief introduction to the
theory of CS and then describe the motivational factors behind the potential
use of CS in WSN applications. Then, we identify three main areas along which
the standard CS framework is extended so that CS can be efficiently applied to
solve a variety of problems specific to WSNs. In particular, we emphasize on
the significance of extending the CS framework to (i). take communication
constraints into account while designing projection matrices and reconstruction
algorithms for signal reconstruction in centralized as well in decentralized
settings, (ii) solve a variety of inference problems such as detection,
classification and parameter estimation, with compressed data without signal
reconstruction and (iii) take practical communication aspects such as
measurement quantization, physical layer secrecy constraints, and imperfect
channel conditions into account. Finally, open research issues and challenges
are discussed in order to provide perspectives for future research directions
Learning Efficient Anomaly Detectors from -NN Graphs
We propose a non-parametric anomaly detection algorithm for high dimensional
data. We score each datapoint by its average -NN distance, and rank them
accordingly. We then train limited complexity models to imitate these scores
based on the max-margin learning-to-rank framework. A test-point is declared as
an anomaly at -false alarm level if the predicted score is in the
-percentile. The resulting anomaly detector is shown to be
asymptotically optimal in that for any false alarm rate , its decision
region converges to the -percentile minimum volume level set of the
unknown underlying density. In addition, we test both the statistical
performance and computational efficiency of our algorithm on a number of
synthetic and real-data experiments. Our results demonstrate the superiority of
our algorithm over existing -NN based anomaly detection algorithms, with
significant computational savings.Comment: arXiv admin note: text overlap with arXiv:1405.053
Transform coder identification based on quantization footprints and lattice theory
Transform coding is routinely used for lossy compression of discrete sources
with memory. The input signal is divided into N-dimensional vectors, which are
transformed by means of a linear mapping. Then, transform coefficients are
quantized and entropy coded. In this paper we consider the problem of
identifying the transform matrix as well as the quantization step sizes. We
study the challenging case in which the only available information is a set of
P transform decoded vectors. We formulate the problem in terms of finding the
lattice with the largest determinant that contains all observed vectors. We
propose an algorithm that is able to find the optimal solution and we formally
study its convergence properties. Our analysis shows that it is possible to
identify successfully both the transform and the quantization step sizes when P
>= N + d where d is a small integer, and the probability of failure decreases
exponentially to zero as P - N increases.Comment: Submitted to IEEE Transactions on Information Theor
Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures
Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs
Reliable OFDM Receiver with Ultra-Low Resolution ADC
The use of low-resolution analog-to-digital converters (ADCs) can
significantly reduce power consumption and hardware cost. However, their
resulting severe nonlinear distortion makes reliable data transmission
challenging. For orthogonal frequency division multiplexing (OFDM)
transmission, the orthogonality among subcarriers is destroyed. This
invalidates conventional OFDM receivers relying heavily on this orthogonality.
In this study, we move on to quantized OFDM (Q-OFDM) prototyping implementation
based on our previous achievement in optimal Q-OFDM detection. First, we
propose a novel Q-OFDM channel estimator by extending the generalized Turbo
(GTurbo) framework formerly applied for optimal detection. Specifically, we
integrate a type of robust linear OFDM channel estimator into the original
GTurbo framework and derive its corresponding extrinsic information to
guarantee its convergence. We also propose feasible schemes for automatic gain
control, noise power estimation, and synchronization. Combined with the
proposed inference algorithms, we develop an efficient Q-OFDM receiver
architecture. Furthermore, we construct a proof-of-concept prototyping system
and conduct over-the-air (OTA) experiments to examine its feasibility and
reliability. This is the first work that focuses on both algorithm design and
system implementation in the field of low-resolution quantization
communication. The results of the numerical simulation and OTA experiment
demonstrate that reliable data transmission can be achieved.Comment: 14 pages, 17 figures; accepted by IEEE Transactions on Communication
Query-driven learning for predictive analytics of data subspace cardinality
Fundamental to many predictive analytics tasks is the ability to estimate the cardinality (number of data items) of multi-dimensional data subspaces, defined by query selections over datasets. This is crucial for data analysts dealing with, e.g., interactive data subspace explorations, data subspace visualizations, and in query processing optimization. However, in many modern data systems, predictive analytics may be (i) too costly money-wise, e.g., in clouds, (ii) unreliable, e.g., in modern Big Data query engines, where accurate statistics are difficult to obtain/maintain, or (iii) infeasible, e.g., for privacy issues. We contribute a novel, query-driven, function estimation model of analyst-defined data subspace cardinality. The proposed estimation model is highly accurate in terms of prediction and accommodating the well-known selection queries: multi-dimensional range and distance-nearest neighbors (radius) queries. Our function estimation model: (i) quantizes the vectorial query space, by learning the analysts’ access patterns over a data space, (ii) associates query vectors with their corresponding cardinalities of the analyst-defined data subspaces, (iii) abstracts and employs query vectorial similarity to predict the cardinality of an unseen/unexplored data subspace, and (iv) identifies and adapts to possible changes of the query subspaces based on the theory of optimal stopping. The proposed model is decentralized, facilitating the scaling-out of such predictive analytics queries. The research significance of the model lies in that (i) it is an attractive solution when data-driven statistical techniques are undesirable or infeasible, (ii) it offers a scale-out, decentralized training solution, (iii) it is applicable to different selection query types, and (iv) it offers a performance that is superior to that of data-driven approaches
Prediction of Transformed (DCT) Video Coding Residual for Video Compression
Video compression has been investigated by means of analysis-synthesis, and
more particularly by means of inpainting. The first part of our approach has
been to develop the inpainting of DCT coefficients in an image. This has shown
good results for image compression without overpassing todays compression
standards like JPEG. We then looked at integrating the same approach in a video
coder, and in particular in the widely used H264 AVC standard coder, but the
same approach can be used in the framework of HEVC. The originality of this
work consists in cancelling at the coder, then automatically restoring, at the
decoder, some well chosen DCT residual coefficients. For this purpose, we have
developed a restoration model of transformed coefficients. By using a total
variation based model, we derive conditions for the reconstruction of
transformed coefficients that have been suppressed or altered. The main purpose
here, in a video coding context, is to improve the ratedistortion performance
of existing coders. To this end DCT restoration is used as an additional
prediction step to the spatial prediction of the transformed coefficients,
based on an image regularization process. The method has been successfully
tested with the H.264 AVC video codec standard.Comment: 10 pages, 12 figure
Quality Adaptive Low-Rank Based JPEG Decoding with Applications
Small compression noises, despite being transparent to human eyes, can
adversely affect the results of many image restoration processes, if left
unaccounted for. Especially, compression noises are highly detrimental to
inverse operators of high-boosting (sharpening) nature, such as deblurring and
superresolution against a convolution kernel. By incorporating the non-linear
DCT quantization mechanism into the formulation for image restoration, we
propose a new sparsity-based convex programming approach for joint compression
noise removal and image restoration. Experimental results demonstrate
significant performance gains of the new approach over existing image
restoration methods
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