2,533 research outputs found
A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images
Predictive coding is attractive for compression onboard of spacecrafts thanks
to its low computational complexity, modest memory requirements and the ability
to accurately control quality on a pixel-by-pixel basis. Traditionally,
predictive compression focused on the lossless and near-lossless modes of
operation where the maximum error can be bounded but the rate of the compressed
image is variable. Rate control is considered a challenging problem for
predictive encoders due to the dependencies between quantization and prediction
in the feedback loop, and the lack of a signal representation that packs the
signal's energy into few coefficients. In this paper, we show that it is
possible to design a rate control scheme intended for onboard implementation.
In particular, we propose a general framework to select quantizers in each
spatial and spectral region of an image so as to achieve the desired target
rate while minimizing distortion. The rate control algorithm allows to achieve
lossy, near-lossless compression, and any in-between type of compression, e.g.,
lossy compression with a near-lossless constraint. While this framework is
independent of the specific predictor used, in order to show its performance,
in this paper we tailor it to the predictor adopted by the CCSDS-123 lossless
compression standard, obtaining an extension that allows to perform lossless,
near-lossless and lossy compression in a single package. We show that the rate
controller has excellent performance in terms of accuracy in the output rate,
rate-distortion characteristics and is extremely competitive with respect to
state-of-the-art transform coding
Universally Typical Sets for Ergodic Sources of Multidimensional Data
We lift important results about universally typical sets, typically sampled
sets, and empirical entropy estimation in the theory of samplings of discrete
ergodic information sources from the usual one-dimensional discrete-time
setting to a multidimensional lattice setting. We use techniques of packings
and coverings with multidimensional windows to construct sequences of
multidimensional array sets which in the limit build the generated samples of
any ergodic source of entropy rate below an with probability one and
whose cardinality grows at most at exponential rate .Comment: 15 pages, 1 figure. To appear in Kybernetika. This replacement
corrects typos and slightly strengthens the main theore
Coding of non-stationary sources as a foundation for detecting change points and outliers in binary time-series
An interesting scheme for estimating and adapting distributions in real-time for non-stationary data has recently been the focus of study for several different tasks relating to time series and data mining, namely change point detection, outlier detection and online compression/sequence prediction. An appealing feature is that unlike more sophisticated procedures, it is as fast as the related stationary procedures which are simply modified through discounting or windowing. The discount scheme makes older observations lose their influence on new predictions. The authors of this article recently used a discount scheme for introducing an adaptive version of the Context Tree Weighting compression algorithm. The mentioned change point and outlier detection methods rely on the changing compression ratio of an online compression algorithm. Here we are beginning to provide theoretical foundations for the use of these adaptive estimation procedures that have already shown practical promise
Universal Compressed Sensing
In this paper, the problem of developing universal algorithms for compressed
sensing of stochastic processes is studied. First, R\'enyi's notion of
information dimension (ID) is generalized to analog stationary processes. This
provides a measure of complexity for such processes and is connected to the
number of measurements required for their accurate recovery. Then a minimum
entropy pursuit (MEP) optimization approach is proposed, and it is proven that
it can reliably recover any stationary process satisfying some mixing
constraints from sufficient number of randomized linear measurements, without
having any prior information about the distribution of the process. It is
proved that a Lagrangian-type approximation of the MEP optimization problem,
referred to as Lagrangian-MEP problem, is identical to a heuristic
implementable algorithm proposed by Baron et al. It is shown that for the right
choice of parameters the Lagrangian-MEP algorithm, in addition to having the
same asymptotic performance as MEP optimization, is also robust to the
measurement noise. For memoryless sources with a discrete-continuous mixture
distribution, the fundamental limits of the minimum number of required
measurements by a non-universal compressed sensing decoder is characterized by
Wu et al. For such sources, it is proved that there is no loss in universal
coding, and both the MEP and the Lagrangian-MEP asymptotically achieve the
optimal performance
- …