13 research outputs found
Correlation between Channel State and Information Source with Empirical Coordination Constraint
Correlation between channel state and source symbol is under investigation
for a joint source-channel coding problem. We investigate simultaneously the
lossless transmission of information and the empirical coordination of channel
inputs with the symbols of source and states. Empirical coordination is
achievable if the sequences of source symbols, channel states, channel inputs
and channel outputs are jointly typical for a target joint probability
distribution. We characterize the joint distributions that are achievable under
lossless decoding constraint. The performance of the coordination is evaluated
by an objective function. For example, we determine the minimal distortion
between symbols of source and channel inputs for lossless decoding. We show
that the correlation source/channel state improves the feasibility of the
transmission.Comment: Conference IEEE ITW 201
Randomized Quantization and Source Coding with Constrained Output Distribution
This paper studies fixed-rate randomized vector quantization under the
constraint that the quantizer's output has a given fixed probability
distribution. A general representation of randomized quantizers that includes
the common models in the literature is introduced via appropriate mixtures of
joint probability measures on the product of the source and reproduction
alphabets. Using this representation and results from optimal transport theory,
the existence of an optimal (minimum distortion) randomized quantizer having a
given output distribution is shown under various conditions. For sources with
densities and the mean square distortion measure, it is shown that this optimum
can be attained by randomizing quantizers having convex codecells. For
stationary and memoryless source and output distributions a rate-distortion
theorem is proved, providing a single-letter expression for the optimum
distortion in the limit of large block-lengths.Comment: To appear in the IEEE Transactions on Information Theor
Statistical Atmospheric Parameter Retrieval Largely Benefits from Spatial-Spectral Image Compression
The Infrared Atmospheric Sounding Interferometer
(IASI) is flying on board of the Metop satellite series, which is
part of the EUMETSAT Polar System (EPS). Products obtained
from IASI data represent a significant improvement in the
accuracy and quality of the measurements used for meteorological models. Notably, IASI collects rich spectral information to
derive temperature and moisture profiles –among other relevant
trace gases–, essential for atmospheric forecasts and for the
understanding of weather. Here, we investigate the impact of
near-lossless and lossy compression on IASI L1C data when
statistical retrieval algorithms are later applied. We search for
those compression ratios that yield a positive impact on the
accuracy of the statistical retrievals. The compression techniques
help reduce certain amount of noise on the original data and,
at the same time, incorporate spatial-spectral feature relations in
an indirect way without increasing the computational complexity.
We observed that compressing images, at relatively low bitrates, improves results in predicting temperature and dew point
temperature, and we advocate that some amount of compression
prior to model inversion is beneficial. This research can benefit
the development of current and upcoming retrieval chains in
infrared sounding and hyperspectral sensors
Joint Empirical Coordination of Source and Channel
In a decentralized and self-configuring network, the communication devices
are considered as autonomous decision-makers that sense their environment and
that implement optimal transmission schemes. It is essential that these
autonomous devices cooperate and coordinate their actions, to ensure the
reliability of the transmissions and the stability of the network. We study a
point-to-point scenario in which the encoder and the decoder implement
decentralized policies that are coordinated. The coordination is measured in
terms of empirical frequency of symbols of source and channel. The encoder and
the decoder perform a coding scheme such that the empirical distribution of the
symbols is close to a target joint probability distribution. We characterize
the set of achievable target probability distributions for a point-to-point
source-channel model, in which the encoder is non-causal and the decoder is
strictly causal i.e., it returns an action based on the observation of the past
channel outputs. The objectives of the encoder and of the decoder, are captured
by some utility function, evaluated with respect to the set of achievable
target probability distributions. In this article, we investigate the
maximization problem of a utility function that is common to both encoder and
decoder. We show that the compression and the transmission of information are
particular cases of the empirical coordination.Comment: accepted to IEEE Trans. on I
Joint universal lossy coding and identification of stationary mixing sources with general alphabets
We consider the problem of joint universal variable-rate lossy coding and
identification for parametric classes of stationary -mixing sources with
general (Polish) alphabets. Compression performance is measured in terms of
Lagrangians, while identification performance is measured by the variational
distance between the true source and the estimated source. Provided that the
sources are mixing at a sufficiently fast rate and satisfy certain smoothness
and Vapnik-Chervonenkis learnability conditions, it is shown that, for bounded
metric distortions, there exist universal schemes for joint lossy compression
and identification whose Lagrangian redundancies converge to zero as as the block length tends to infinity, where is the
Vapnik-Chervonenkis dimension of a certain class of decision regions defined by
the -dimensional marginal distributions of the sources; furthermore, for
each , the decoder can identify -dimensional marginal of the active
source up to a ball of radius in variational distance,
eventually with probability one. The results are supplemented by several
examples of parametric sources satisfying the regularity conditions.Comment: 16 pages, 1 figure; accepted to IEEE Transactions on Information
Theor
Rate-Distortion via Markov Chain Monte Carlo
We propose an approach to lossy source coding, utilizing ideas from Gibbs
sampling, simulated annealing, and Markov Chain Monte Carlo (MCMC). The idea is
to sample a reconstruction sequence from a Boltzmann distribution associated
with an energy function that incorporates the distortion between the source and
reconstruction, the compressibility of the reconstruction, and the point sought
on the rate-distortion curve. To sample from this distribution, we use a `heat
bath algorithm': Starting from an initial candidate reconstruction (say the
original source sequence), at every iteration, an index i is chosen and the
i-th sequence component is replaced by drawing from the conditional probability
distribution for that component given all the rest. At the end of this process,
the encoder conveys the reconstruction to the decoder using universal lossless
compression. The complexity of each iteration is independent of the sequence
length and only linearly dependent on a certain context parameter (which grows
sub-logarithmically with the sequence length). We show that the proposed
algorithms achieve optimum rate-distortion performance in the limits of large
number of iterations, and sequence length, when employed on any stationary
ergodic source. Experimentation shows promising initial results. Employing our
lossy compressors on noisy data, with appropriately chosen distortion measure
and level, followed by a simple de-randomization operation, results in a family
of denoisers that compares favorably (both theoretically and in practice) with
other MCMC-based schemes, and with the Discrete Universal Denoiser (DUDE).Comment: 35 pages, 16 figures, Submitted to IEEE Transactions on Information
Theor
Joint Universal Lossy Coding and Identification of Stationary Mixing Sources With General Alphabets
Abstract-In this paper, we consider the problem of joint universal variable-rate lossy coding and identification for parametric classes of stationary -mixing sources with general (Polish) alphabets. Compression performance is measured in terms of Lagrangians, while identification performance is measured by the variational distance between the true source and the estimated source. Provided that the sources are mixing at a sufficiently fast rate and satisfy certain smoothness and Vapnik-Chervonenkis (VC) learnability conditions, it is shown that, for bounded metric distortions, there exist universal schemes for joint lossy compression and identification whose Lagrangian redundancies converge to zero as V n log n=n as the block length n tends to infinity, where V n is the VC dimension of a certain class of decision regions defined by the n-dimensional marginal distributions of the sources; furthermore, for each n, the decoder can identify n-dimensional marginal of the active source up to a ball of radius O( V n log n=n) in variational distance, eventually with probability one. The results are supplemented by several examples of parametric sources satisfying the regularity conditions
Statistical atmospheric parameter retrieval largely benefits from spatial-spectral image compression
The infrared atmospheric sounding interferometer (IASI) is flying on board of the Metop satellite series, which is part of the EUMETSAT Polar System. Products obtained from IASI data represent a significant improvement in the accuracy and quality of the measurements used for meteorological models. Notably, the IASI collects rich spectral information to derive temperature and moisture profiles, among other relevant trace gases, essential for atmospheric forecasts and for the understanding of weather. Here, we investigate the impact of near-lossless and lossy compression on IASI L1C data when statistical retrieval algorithms are later applied. We search for those compression ratios that yield a positive impact on the accuracy of the statistical retrievals. The compression techniques help reduce certain amount of noise on the original data and, at the same time, incorporate spatial-spectral feature relations in an indirect way without increasing the computational complexity. We observed that compressing images, at relatively low bit rates, improves results in predicting temperature and dew point temperature, and we advocate that some amount of compression prior to model inversion is beneficial. This research can benefit the development of current and upcoming retrieval chains in infrared sounding and hyperspectral sensors