Stationarity in the Realizations of the Causal Rate-Distortion Function for One-Sided Stationary Sources

04/11/2018 ∙ by Milan S. Derpich, et al. ∙ Aalborg University 0

This paper derives novel results on the characterization of the the causal information rate-distortion function (IRDF) R_c^it(D) for arbitrarily-distributed one-sided stationary κ-th order Markov source x(1),x(2),.... It is first shown that Gorbunov and Pinsker's results on the stationarity of the realizations to the causal IRDF (stated for two-sided stationary sources) do not apply to the commonly used family of asymptotic average single-letter (AASL) distortion criteria. Moreover, we show that, in general, a reconstruction sequence cannot be both jointly stationary with a one-sided stationary source sequence and causally related to it. This implies that, in general, the causal IRDF for one-sided stationary sources cannot be realized by a stationary distribution. However, we prove that for an arbitrarily distributed one-sided stationary source and a large class of distortion criteria (including AASL), the search for R_c^it(D) can be restricted to distributions which yield the output sequence y(1), y(2),... jointly stationary with the source after κ samples. Finally, we improve the definition of the stationary causal IRDF R_c^it(D) previously introduced by Derpich and Østergaard for two-sided Markovian stationary sources and show that R_c^it(D) for a two-sided source ...,x(-1),x(0),x(1),... equals R_c^it(D) for the associated one-sided source x(1), x(2),.... This implies that, for the Gaussian quadratic case, the practical zero-delay encoder-decoder pairs proposed by Derpich and Østergaard for approaching R_c^it(D) achieve an operational data rate which exceeds R_c^it(D) by less than 1+0.5 _2(2 π e /12) ≃ 1.254 bits per sample.



There are no comments yet.


page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

I Introduction

The information RDF (IRDF) for a given one-sided random source process can be defined as the infimum of the mutual information rate [gray--11, Section 8.2]


between source and reconstruction such that a given fidelity criterion does not exceed a distortion value  [berger71, covtho06, gray--11]. If one adds to this definition the restriction that the decoder output can only depend causally upon the source, one obtains what is known as the causal [neugil82, derost12], non-anticipative [gorpin73, pingor87, chasta14] or sequential IRDF [tatiko03, tatsah04, tankim15]. All these are equivalent and will be denoted as , defined in terms of the mutual information  [gray--11, covtho06] as


where the infimum is taken over all joint distributions of


such that the causality Markov chains (which will be referred to as the

short causality constraint)


hold and which yield distortion not greater than , for some fidelity criterion. Notice that, if one is given a two-sided random source process instead, and one is interested only in encoding and reconstructing the samples , then the causality constraints may be stated as


as done in [gorpin73, pingor87, derost12]. This notion of causality will be referred to as the long causality constraint.

The motivation for considering in this work one-sided instead of two-sided sequences (and thus (3) instead of (4)) arises from the aim of building encoder-decoder systems which operate with zero delay (the same motivation behind the causality constraint). To see this, notice that the causality constraint (4) for two-sided sources corresponds to the situation in which source samples in the infinite past exist and are available to the encoder. This may require an infinite delay before actually beginning to encode and decode. By contrast, the causality constraint (3) describes the case when the source is a one-sided process and depends only upon (as in [chasta14, woolin17]).

Remark 1.

It is important to highlight at this point that even though the causality condition (3) can also be applied to a two-sided source process , it would not ensure causality in that case. To see why, consider the situation in which is a binary i.i.d. source where each takes the values or

with equal probability. Suppose

is built as , where denotes the exclusive “OR” operator. It is easy to see that satisfies (3), even though depends non-causally on .

The above observation reveals that if the source is two sided but only the samples are encoded and the decoded process is one-sided (), then one needs to impose instead the (more general) causality constraint


which implies (3). Besides causality, these Markov chains guarantee that even if the source is a two-sided process, its encoding and reconstruction proceeds as if it were a one-sided process.

Notice that (5) implies (3) and (4). For this reason, (5) will be referred to as the strong causality constraints.

As we shall see in sections III and IV, this situation, where at time the encoder can take only as input, entails significant challenges due to the unavoidable need to deal with transient phenomena.

The operational significance of stems from its relation to the causal operational RDF (ORDF), denoted as . The latter is defined as the infimum of the average data-rates which are achievable by a sequence of causal encoder-decoder functions [neugil82, derost12] yielding a distortion not greater than . Characterizing is important because every zero-delay source code (suitable for applications such as low-delay streaming [chenwu15] or networked control [naifag07, silder16]) must be causal.

An IRDF is said to be achievable if it equals the ORDF under the same constraints [berger71, covtho06]. As far as the authors are aware, the achievability of has not been demonstrated yet, for any source and distortion measure, and thus the gap between and is unknown in general. However, it is known that [derost12, Section II]


and for Gaussian sources it is possible to construct causal codes with an operational data rate exceeding by less than (approximately) 0.254 bits/sample (1.254 bits/sample for zero-delay codes), once the statistics which realize the latter are known [derost12]. This underlines the importance of studying the causal IRDF .

To the best of the authors’ knowledge, no closed-form expressions are known for , except when considering mean-squared-error (MSE) distortion and for Gaussian i.i.d. or Gaussian auto-regressive (AR)-1 sources, either scalar [derost12, Section IV]

or vector valued 

[staost17]111Although for i.i.d. sources and for a single-letter distortion criterion a realization of the (non-causal) RDF satisfies causality [covtho06, berger71], the formulas available in the literature for expressing it require numerical iterative procedures and cannot be regarded as “closed-form” except for the Gaussian case and MSE distortion.. However, there exist various structural properties of the causal IRDF that have been found in literature when admits (or is assumed to admit) a stationary realization.

Indeed, the stationarity of the realizations of the causal IRDF has played a crucial role in simplifying the computation of for Gaussian 1-st order Markovian sources and MSE distortion in [tanaka15]. It has also been a key implicit assumption in [tatsah04], and an explicit assumption in works such as [chasta14] and [derost12]. In particular, for a stationary two-sided random source [derost12, Definition 6] introduced the stationary causal IRDF


where the infimum is taken over all distributions of given which yield a one-sided reconstruction processes jointly stationary with , satisfying (4) and an asymptotic average MSE distortion constraint on . For the case of a Gaussian source, it was shown in [derost12] that an operational data-rate exceeding by less than bits/sample was achievable using a entropy-coded subtractively dithered uniform quantizer (ECSDUQ) surrounded by linear time-invariant (LTI) filters operating in steady state. These examples illustrate the relevance of determining whether (or in which cases) the causal IRDF admits a stationary realization.

To the best of our knowledge, the only work which has given an answer to this question in a general framework is [gorpin73]. Under a set of assumptions (discussed in Section II below), it is shown in [gorpin73, Theorem 4] that the search for the causal IRDF for a large class of two-sided sources and distortion criteria can be restricted to reconstructions which are jointly stationary with the source. Unfortunately, as we show in Section II-B, the assumptions on the fidelity criteria utilized in [gorpin73] leave out some common distortions (such as the family of asymptotic average single-letter fidelity criteria), and the statement of [gorpin73, Theorem 4] contains an assumption whose validity has to be proved. More importantly, the entire analysis of [gorpin73] is built for two-sided processes (using the causality constraint (4)), which opens the question of whether its results could apply to one-sided processes as well, with the causality constraint (3).

In this paper we give an answer to these questions and use the results to prove some novel properties of the causal IRDF associated with the stationarity of its realizations. Specifically, our main contributions are the following:

  1. We show in Theorem 2 that if a pair of one-sided random processes is jointly stationary, with the latter depending causally on the former according to (5) (but otherwise arbitrarily distributed), then it must also satisfy the Markov chains


    which is a fairly restrictive condition. In particular, as we show in Theorem 3, if are jointly Gaussian and depends causally upon , then joint stationarity implies is an i.i.d. or 1st-order Markovian process. This stands in stark contrast with what was shown in [gorpin73] for two-sided stationary processes and constitutes a counterexample of what is stated in [stakou15, Theorem III.6].

  2. Despite the above, we show in Theorem 4 that for any -th order Markovian one-sided stationary source and a large class of distortion constraints, the search for the causal IRDF (as defined in (2)) can be restricted to output sequences causally related to the source and jointly stationary with it after samples, and such that . We refer to such pairs of processes as being -quasi-jointly stationary (-QJS) (this notion is formally introduced in Definition 2 below). A consequence of this result is that for any -th order two-sided Markovian stationary source , equals for the corresponding one-sided stationary source . The relevance of this finding is that for Gaussian stationary sources and asymptotic MSE distortion, an operational data rate exceeding (and thus ) by less than approximately bits/sample, when operating causally, and bit/sample, in zero-delay operation, is achievable by using a scalar ECSDUQ as in [derost12].

The remainder of this paper begins with Section II, in which the assumptions leading to [gorpin73, Theorem 4] are revisited and the limitations of that theorem are discussed. In Section III we prove that, in general, it is not possible to have two one-sided processes which are jointly stationary and, at the same time, satisfy the causality constraint (3). Section IV presents our main theorem (Theorem 4), which shows that the search for the causal IRDF for one-sided -th order Markovian stationary sources can be restricted to -QJS processes. Finally, Section LABEL:sec:Conclusions draws the main conclusions of this work. All proofs are presented in section LABEL:sec:appendix (the Appendix), which also contains some technical lemmas required by these proofs.


denotes the real numbers, denotes the integers, is the set of natural numbers (positive integers), and . For every , the ceiling operator yields the smallest integer not less than

. We use non-italic letters for scalar random variables, such as

. Random sequences are denoted as . For a random (one-sided) process we will sometimes use the short-hand notation wherever this meaning is clear from the context. When convenient, we write a random sequence , , as the column vector (the indices and are swapped so that the smallest index goes above the largest one, thus mimicking the usual index order in a column vector). The entry on the -th row and -th column of a matrix is denoted as , with being the sub-matrix of containing its rows to , .

For a random element in a given alphabet (set) , we write to denote a sigma-algebra associated with and

to denote its probability distribution (or probability measure). We write

to describe the fact that has the same probability distribution as , and to state that and are independent. We write the condition in which two random elements are independent given a third random element using the Markov chain notation . If is a set of probability distributions, then denotes the set of all random elements whose probability distribution belongs to . The expectation operator is denoted as . We write as a shorthand for . The mutual information between two random elements is defined as [gray--11, Lemma 7.14]


where the supremum is over all quantizers and of and , and , and , are the joint and marginal distributions of and , respectively. If have joint and marginal probability density functions (PDFs) , and , respectively, then [covtho06]

The conditional mutual information

is defined via the chain-rule (cr) of mutual information

. The mutual information rate between two processes and is defined as in (1

). The variance of a real-valued random variable

is denoted as . The auto-correlation function of a random process is denoted , , .

The following properties of the mutual information involving any random elements will be utilized and referred to throughout this work:

P 1.

, with equality if and only if .

P 2.

, with equality if and only if .

We will also make use of the following fact:

Fact 1.

Let be three random elements with an arbitrary joint distribution. Then, there exists a random element (equivalently, a joint distribution ) such that


Ii Revisiting [gorpin73] and its Inapplicability to One-Sided Sources

In order to assess whether (or to what extent) [gorpin73, Theorem 4] could provide support to the stationarity assumptions made in, e.g. [tatsah04, dersil08, chasta14, stakou15], it is necessary to take a closer look at the assumptions made in [gorpin73] and the statement of its Theorem 4. For that purpose, the first part of this section is an exposition of the definitions and assumptions leading to [gorpin73, Theorem 4].222We believe this re-exposition of [gorpin73] to be valuable in itself since on the one hand, it selects the minimal set of notions required to formulate and understand its Theorem 4, and on the other hand, it provides an arguably clearer presentation than the one found in [gorpin73] (an English translation from Russian), which is not easy to read due to its notation, some mathematical typos and the low resolution of its available digitized form. The second part is an analysis which reveals the limitations of [gorpin73, Theorem 4] and its inapplicability to the case in which the source and reconstruction are one-sided processes. At the same time, this section also introduces definitions and part of the notation to be utilized in the remainder of this paper (for convenience, a summary of these is presented in Table I below).

Ii-a A Brief Review of [gorpin73]

Throughout [gorpin73], the search in the infimizations associated with various types of “nonanticipatory” (i.e., causal) rate-distortion functions is stated over sets of joint probability distributions between source and reconstruction (as opposed to the usual definitions, in which the search is over conditional distributions, see (2) and [covtho06, Chapter 10][berger71]). Since the distribution of the source is given, it is required that for every , all the joint distributions to be considered yield having the same (given) distribution of the source for the corresponding block, say . This requirement can be formalized as requiring that , for a set of admissible joint distributions defined as


where and are, respectively, the alphabets to which and belong. In [gorpin73], this admissibility requirement is embedded in the definition of the sets of distributions which meet the distortion constraint, described next.

The fidelity criterion for every pair of integers333The analysis in [gorpin73] considered both discrete- and continuous-time processes, but here we only refer to the discrete-time scenario. is expressed in [gorpin73] as requiring to belong to a non-empty set of distributions (hereafter referred to as distortion-feasible set) , a condition written as . In this definition, the number represents an admissible distortion level. Notice that such general formulation of a fidelity criteria does not need a distortion function and does not necessarily involve an expectation.

As mentioned above, the admissibility requirement is expressed in the distortion-feasible sets in [gorpin73, eqn. (2.1)]. The latter equation can be written as


In [gorpin73, eqs. (2.4) and (2.5)], the distortion-feasible sets are assumed to satisfy the “concatenation” condition


With this, [gorpin73, eqn. (2.9)] defined the “nonanticipatory epsilon entropy” of the set of distributions444The actual term employed in [gorpin73] is “nonanticipatory epsilon entropy of the message ” where the term “message” refers to the random ensembles in . as


where the infimum is taken over all pairs of random sequences such that the causality Markov chains


are satisfied. Then [gorpin73, eq. (2.13)] defines the “nonanticipatory message generation rate” as


(when the limit exists). An alternative “nonanticipatory message generation rate” is also considered in [gorpin73] by defining the set of distortion-admissible process distributions as follows:

Definition 1.

The set consists of all two-sided random process pairs for which there exist integers such that and


With this, [gorpin73, eq. (2.12)] defines


(when the limit exists), where the infimum is taken over all pairs of processes satisfying the causality Markov chains


Notice that these Markov chains imply (4) and differ from the latter in that here the reconstruction is a two-sided random process.

Now assume that and , for all , for some alphabets and . Define, for any given non-negative sequence , such that , the distribution


We can now re-state Theorem 4 in [gorpin73] as follows:

Theorem 1 (Theorem 4 in[gorpin73]).

Suppose that

  1. is stationary.

  2. Stationary distortion-feasible sets: For every , , and are identical sets.555In [gorpin73] this condition together with the stationarity of is referred to as “a stationary source” (see its description between (2.8) and (2.9) in [gorpin73]).

  3. The concatenation condition (14) holds.

  4. .

  5. For every set of non-negative numbers , such that ,


    where the processes are distributed according to (21).

Then, the analysis of the lower bound in (19) can be confined to jointly stationary pairs of random processes satisfying the causality constraint (20).

For convenience, Table I presents a summary of the definitions and notation described so far, together with some which will be defined in the following sections.

The joint probability distribution of
The set of all joint distributions such that the associated marginal distribution equals the given distribution of the source sequence , i.e., (see (12)).
Distortion-feasible set. The set of all joint distributions which satisfy a given constraint given by (see comments before (12)).
The set of all pairs of sequences such that . (See also the Notation subsection at the end of Section I.)
Generic distortion-feasible set of probability distributions for pairs of one-sided processes . In this paper, we state some minimal conditions on in Assumption 1 and some additional structural properties in Assumption 2.
, The set of all joint distributions of pairs of one-sided random processes such that are jointly stationary and (see Definition 2).
and The sets of causally related one-sided pairs of -sequences (see Definition 3).
The set of one-sided pairs of processes causally related according to the short causality constraint (3) (see Definition 3).
The set of causal distributions for processes of the form . Such processes satisfy the long causality constraint (4) (see Definition LABEL:def:Overline_RCitd_redef).
Table I: Summary of the main symbols utilized in this paper.

Ii-B Analysis of Theorem 1 and its Inapplicability to One-Sided Sources

We now discuss three limitations of Theorem 1 which are relevant when trying to establish whether the causal IRDF of a one-sided stationary source admits a stationary realization.

Limitation 1

The first obvious limitation is that even if source and reconstruction are two-sided processes, every distortion criterion which considers only their “positive-time” part cannot be expressed by a distortion-feasible set given by Definition 1 if the sets satisfy condition ii) in Theorem 1. To see this, notice that if , then such distortion criterion (which neglects non-positive times) would require to admit all joint probability distributions satisfying (13). Combining this with condition ii) in Theorem 1 yields that every set with , which amounts to imposing no restriction on the distortion at all.

It is natural to think that such elemental shortcoming could be avoided by simply replacing condition ii) in Theorem 1 by a one-sided version of the form:

For every , such that : and are identical sets. (23)

Leaving aside the fact that this alternative condition is not sufficient for Theorem 1 to hold, it is worth pointing out that using (23), the commonly utilized family of asymptotic single-letter fidelity criteria [berger71] can not be expressed by a distortion-feasible set given by Definition 1, as the following lemma shows (its proof can be found in Appendix LABEL:proof:of_lem_Wsp_D_cannot_represent_asymptotic_SLDC).

Lemma 1.

Let be any given distortion functional which takes as argument a joint distribution and yields a non-negative real value. Let be the set of all pairs of processes where is stationary, with pair-wise distributions which satisfy the asymptotic single-letter fidelity criterion


Then, there doesn’t exist an infinite collection of distortion-feasible sets satisfying (23) such that the associated given by Definition 1 satisfies .

Limitation 2

The second limitation associated with Theorem 1 is that its application requires one to prove its condition iv), i.e., the unproven supposition that , holds. The only work we are aware of which builds upon Theorem 1 is [stakou15], and, accordingly, [stakou15] provides [stakou15, Theorem III.5], which states that a similar equality holds. Unfortunately, as shown in [derpic17], the proof of [stakou15, Theorem III.5] is flawed.

We note that Lemma 3 in Section IV-A below provides two alternative sufficient conditions for an equality similar to (but for one-sided processes) to hold.

Limitation 3

The third limitation of Theorem 1 for its applicability to one-sided sources is the fact that the entire framework built in [gorpin73] is stated for two-sided processes (and, crucially, for the corresponding causality restriction given by Markov chain (20)). This difference cannot be simply neglected while expecting Theorem 1 to remain valid. Indeed, as we show in the next section (Theorem 2), a pair of random processes can be jointly stationary and at the same time satisfy the causality Markov chain (3) only if is independent of when is given. Moreover, we prove that joint stationarity and causality are incompatible when the source is a -th order Markovian Gaussian one-sided process with .

Iii Conditions for Joint Stationarity and Causality to Hold Together

In this section we address the question of whether there exists a one-sided reconstruction process jointly stationary with a source and which also satisfies the causality constraint (3).

Each source random sample belongs to some given set (source alphabet) and is allowed to have an arbitrary distribution. Recall that a random process , where is the reconstruction alphabet and , is said to be jointly stationary with if and only if, for every , the distribution of does not depend on , for .

The next theorem shows that, for such one-sided processes, joint-stationarity and causality may hold together only if is independent of when is given.

Theorem 2.

If and are jointly stationary and is causally related to according to (3), then


If (25) does not hold for some and if and are jointly stationary, then


does not hold, which corresponds to not satisfying (3) for , completing the proof. ∎

To illustrate how restrictive condition (25) is, the next theorem shows that, for a Gaussian -th order Markovian stationary source , causality and joint stationarity is possible only if is i.i.d. () or . Recall that a random (vector or scalar valued) process is -th order Markovian if is the smallest non-negative integer such that

Theorem 3.

Suppose is a zero-mean Gaussian stationary process, and assume that, for some , are jointly Gaussian and jointly stationary, with being causally related to according to (3). Then is -th order Markovian with .


Since and are jointly Gaussian and the latter depends causally upon the former, it holds that


for some lower triangular matrix having entries . On the other hand, the fact that and are jointly stationary implies that and are Toeplitz matrices. From (28), considering the entries on the first and second rows of and defining

this Toeplitz condition implies that

Therefore, , which for a Gaussian stationary sequence implies that , . For Gaussian random variables the latter is equivalent to the Markov chains , which defines a 1-st order Markovian process (if ) or an i.i.d. process (if ). This completes the proof. ∎

In the next section we will see that if is -th order Markovian, then it is possible to build a pair causally related according to (3) such that is stationary. Moreover, we will show in Theorem 4 below that the minimization associated with the causal IRDF can be restricted to such pairs.

Iv The Set of Quasi-Jointly Stationary Realizations is Sufficient

In this section we show that for any -th order Markovian one-sided stationary source the search for the causal IRDF (as defined in (2) and for a large class of distortion criteria) can be restricted to output sequences causally related to the source, jointly stationary with it after samples, and such that . We refer to such pairs of processes as being quasi-jointly stationary (-QJS), and define the set which contains them as follows:

Definition 2 (Set of quasi-jointly stationary process).

The set of -QJS distributions is composed of all joint distributions of pairs of one-sided random processes which satisfy

are jointly stationary

Notice that corresponds to the set of joint distributions associated with all jointly stationary one-sided process pairs.

As in [gorpin73], we write when the distribution of belongs to the distortion-feasible set , defined as in (13).

One can define a distortion-feasible set for pairs of one-sided processes , say , from the finite-length distortion-feasible sets , in more than one manner. A minimal condition we shall require for such definition is the following.

Assumption 1.

The distortion-feasible set of distributions for pairs of one-sided processes satisfies the following:

  1. If , then has the given probability distribution of the source process, say . That is, (see (12)).

  2. If is any given pair of one-sided processes, and there exists an infinite collection of increasing integers such that, for all , , then .

  3. For any pair of sequences , , and if , then the concatenated processes , satisfy .

Notice that if satisfies this assumption and if the integers in Definition 1 were restricted to be positive, then we would have