1,476 research outputs found
A vector quantization approach to universal noiseless coding and quantization
A two-stage code is a block code in which each block of data is coded in two stages: the first stage codes the identity of a block code among a collection of codes, and the second stage codes the data using the identified code. The collection of codes may be noiseless codes, fixed-rate quantizers, or variable-rate quantizers. We take a vector quantization approach to two-stage coding, in which the first stage code can be regarded as a vector quantizer that “quantizes” the input data of length n to one of a fixed collection of block codes. We apply the generalized Lloyd algorithm to the first-stage quantizer, using induced measures of rate and distortion, to design locally optimal two-stage codes. On a source of medical images, two-stage variable-rate vector quantizers designed in this way outperform standard (one-stage) fixed-rate vector quantizers by over 9 dB. The tail of the operational distortion-rate function of the first-stage quantizer determines the optimal rate of convergence of the redundancy of a universal sequence of two-stage codes. We show that there exist two-stage universal noiseless codes, fixed-rate quantizers, and variable-rate quantizers whose per-letter rate and distortion redundancies converge to zero as (k/2)n -1 log n, when the universe of sources has finite dimension k. This extends the achievability part of Rissanen's theorem from universal noiseless codes to universal quantizers. Further, we show that the redundancies converge as O(n-1) when the universe of sources is countable, and as O(n-1+ϵ) when the universe of sources is infinite-dimensional, under appropriate conditions
Variable dimension weighted universal vector quantization and noiseless coding
A new algorithm for variable dimension weighted universal coding is introduced. Combining the multi-codebook system of weighted universal vector quantization (WUVQ), the partitioning technique of variable dimension vector quantization, and the optimal design strategy common to both, variable dimension WUVQ allows mixture sources to be effectively carved into their component subsources, each of which can then be encoded with the codebook best matched to that source. Application of variable dimension WUVQ to a sequence of medical images provides up to 4.8 dB improvement in signal to quantization noise ratio over WUVQ and up to 11 dB improvement over a standard full-search vector quantizer followed by an entropy code. The optimal partitioning technique can likewise be applied with a collection of noiseless codes, as found in weighted universal noiseless coding (WUNC). The resulting algorithm for variable dimension WUNC is also described
Quadratic optimal functional quantization of stochastic processes and numerical applications
In this paper, we present an overview of the recent developments of
functional quantization of stochastic processes, with an emphasis on the
quadratic case. Functional quantization is a way to approximate a process,
viewed as a Hilbert-valued random variable, using a nearest neighbour
projection on a finite codebook. A special emphasis is made on the
computational aspects and the numerical applications, in particular the pricing
of some path-dependent European options.Comment: 41 page
Vector quantization
During the past ten years Vector Quantization (VQ) has developed from a theoretical possibility promised by Shannon's source coding theorems into a powerful and competitive technique for speech and image coding and compression at medium to low bit rates. In this survey, the basic ideas behind the design of vector quantizers are sketched and some comments made on the state-of-the-art and current research efforts
Greedy vector quantization
We investigate the greedy version of the -optimal vector quantization
problem for an -valued random vector . We show the
existence of a sequence such that minimizes
(-mean quantization error at level induced by
). We show that this sequence produces -rate
optimal -tuples ( the -mean
quantization error at level induced by goes to at rate
). Greedy optimal sequences also satisfy, under natural
additional assumptions, the distortion mismatch property: the -tuples
remain rate optimal with respect to the -norms, .
Finally, we propose optimization methods to compute greedy sequences, adapted
from usual Lloyd's I and Competitive Learning Vector Quantization procedures,
either in their deterministic (implementable when ) or stochastic
versions.Comment: 31 pages, 4 figures, few typos corrected (now an extended version of
an eponym paper to appear in Journal of Approximation
Conditional hitting time estimation in a nonlinear filtering model by the Brownian bridge method
The model consists of a signal process which is a general Brownian
diffusion process and an observation process , also a diffusion process,
which is supposed to be correlated to the signal process. We suppose that the
process is observed from time 0 to at discrete times and aim to
estimate, conditionally on these observations, the probability that the
non-observed process crosses a fixed barrier after a given time . We
formulate this problem as a usual nonlinear filtering problem and use optimal
quantization and Monte Carlo simulations techniques to estimate the involved
quantities
Product Markovian quantization of an R^d -valued Euler scheme of a diffusion process with applications to finance
We introduce a new approach to quantize the Euler scheme of an
-valued diffusion process. This method is based on a Markovian
and componentwise product quantization and allows us, from a numerical point of
view, to speak of {\em fast online quantization} in dimension greater than one
since the product quantization of the Euler scheme of the diffusion process and
its companion weights and transition probabilities may be computed quite
instantaneously. We show that the resulting quantization process is a Markov
chain, then, we compute the associated companion weights and transition
probabilities from (semi-) closed formulas. From the analytical point of view,
we show that the induced quantization errors at the -th discretization step
is a cumulative of the marginal quantization error up to time .
Numerical experiments are performed for the pricing of a Basket call option,
for the pricing of a European call option in a Heston model and for the
approximation of the solution of backward stochastic differential equations to
show the performances of the method
- …