JPEG is the most common format for storing and transmitting photographic images on social network platforms. JPEG image is widely used in people's life because of their low storage space and high visual quality. Secret image sharing (SIS) technology is important to protect image data. Traditional SIS schemes generally focus on spatial images, however there is little research on frequency domain images. In addition, the current tiny research on SIS for JPEG images only focuses on JPEG images with a compression quality factor (QF) of 100. To overcome the limitation of JPEG images in SIS, we propose a meaningful SIS for JPEG images to operate the quantized DCT coefficients of JPEG images. The random elements utilization model is applied to achieve meaningful shadow images. Our proposed scheme has a better quality of the shadow images and the recovered secret image. Experiment results and comparisons indicate the effectiveness of the scheme. The scheme can be used for JPEG images with any compression QF. Besides, the scheme has good characteristics, such as (k,n) threshold, extended shadow images.
Citation: Yue Jiang, Xuehu Yan, Jia Chen, Jingwen Cheng, Jianguo Zhang. Meaningful secret image sharing for JPEG images with arbitrary quality factors[J]. Mathematical Biosciences and Engineering, 2022, 19(11): 11544-11562. doi: 10.3934/mbe.2022538
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JPEG is the most common format for storing and transmitting photographic images on social network platforms. JPEG image is widely used in people's life because of their low storage space and high visual quality. Secret image sharing (SIS) technology is important to protect image data. Traditional SIS schemes generally focus on spatial images, however there is little research on frequency domain images. In addition, the current tiny research on SIS for JPEG images only focuses on JPEG images with a compression quality factor (QF) of 100. To overcome the limitation of JPEG images in SIS, we propose a meaningful SIS for JPEG images to operate the quantized DCT coefficients of JPEG images. The random elements utilization model is applied to achieve meaningful shadow images. Our proposed scheme has a better quality of the shadow images and the recovered secret image. Experiment results and comparisons indicate the effectiveness of the scheme. The scheme can be used for JPEG images with any compression QF. Besides, the scheme has good characteristics, such as (k,n) threshold, extended shadow images.
Let Ω be a bounded domain of RN(N≥3) with smooth boundary ∂Ω. In this paper, we consider the existence of W1,10(Ω)solutions to the following elliptic problem
{−div(M(x,u)∇u)+|∇u|2uθ=f,x∈Ω,u=0,x∈∂Ω, | (1.1) |
where NN−1≤θ<2, M:Ω×R→RN2 is a symmetric Carathéodory matrix function, which satisfies the following assumptions: for some real constants γ>0, α>0,β>0,
|M(x,s)|≤β,M(x,s)ξ⋅ξ≥α(a(x)+|s|)γ|ξ|2, | (1.2) |
for almost every x∈Ω, (s,ξ)∈R×RN, where a(x) is a measurable function, such that
0<ζ≤a(x)≤ρ, | (1.3) |
for some positive constants ζ,ρ.
We note that there are two difficulties in dealing with (1.1), the first one is the fact that, due to hypothesis (1.2), the differential operator A(u)=−div(M(x,u)∇u) is well defined in H10(Ω), but it not coercive on H10(Ω) when u is large enough. Therefore, the classical Leray-Lions theorem cannot be applied even if f is sufficiently regular. The second difficulty is dealing with lower order term which singular natural growth with respect to the gradient. In order to overcome these difficulties, we approximate problem (1.1) by means of truncations in M(x,s) to get a coercive differential operator on H10(Ω).
The existence of W1,10(Ω) solution to elliptic problem has been studied by many authors. Boccardo and Croce [4] proved the existence of W1,10(Ω) solutions to problem
{−div(a(x)∇u(1+|u|)γ)=f,x∈Ω,u=0,x∈∂Ω, |
where a:Ω→R is a measurable function which satisfies (1.3), f∈Lm(Ω) with
m=NN+1−γ(N−1),1N−1<γ<1. |
In the literature [6], the authors considered the existence and regularity of solutions to the following elliptic equation with noncoercivity
{−div(a(x,u)∇u)=f,x∈Ω,u=0,x∈∂Ω, | (1.4) |
where Ω is an open bounded subset of RN(N≥3), f∈Lm(Ω) and a(x,s):Ω×R→R is a Carathéodory function which satisfies
α(1+|s|)γ≤a(x,s)≤β, |
where 0≤γ<1.The existence results of solutions to problem (1.4)are as following:
● There exists a weak solution u∈H10(Ω)∩L∞(Ω) to (1.4) if m>N2.
● There exists a weak solution u∈H10(Ω)∩Lr(Ω) to (1.4) with r=Nm(1−γ)N−2m if
2NN+2−γ(N−2)≤m<N2. |
● There exists a distributional solution u∈W1,q0(Ω) to (1.4) with q=Nm(1−γ)N−m(1+γ)<2 if
NN+1−γ(N−1)<m<2NN+2−γ(N−2). |
In [15], the under the assumption (1.2)-(1.3), Souilah proved the existence results of solutions to problem
{−div(M(x,u)∇u)+|∇u|2uθ=f+λur,x∈Ω,u=0,x∈∂Ω, | (1.5) |
where 0<θ<1,0<r<2−θ,λ>0,f∈Lm(Ω)(m≥1). There exists at least a solution to problem (1.5):
● If 2N2N−θ(N−2)≤m<N2, then u∈H10(Ω)∩L∞(Ω).
● If 1<m<2N2N−θ(N−2), then u∈W1,q0(Ω) with q=Nm(2−θ)N−mθ.
● If m≥N2, then u∈H10(Ω)∩L∞(Ω).
Moreover, the existence of solutions u∈H10(Ω) to problem (1.5) with λ=0 have been obtained in [9]. Some other related results see [1,3,5,7,10,11,12,14,16].
Based on the above research results, the aim of this article is to study the existence of W1,10(Ω) solution to problem (1.1).
In order to state the main results of this paper, the following definition need to be introduced. We use the following notion of distributional solution to problem (1.1).
Definition 1.1. We say that u∈W1,10(Ω) is a distributional solution to problem (1.1) if u>0 in Ω, |∇u|2uθ∈L1(Ω) and
∫ΩM(x,u)∇u⋅∇φ+∫Ω|∇u|2uθφ=∫Ωfφ, |
for every φ∈C∞0(Ω).
Our main results are following:
Theorem 1.2. Assume that (1.2)-(1.3) hold, f∈Lm(Ω) is a nonnegative function with
m=N2N−θ(N−1),NN−1<θ<2. | (1.6) |
Then there exists a distributional solution u∈W1,10(Ω) to problem (1.1).
Remark 1.3. Notice that the result of previous theorem do not depend on γ.
Remark 1.4. Observe that, m>1 if and only if θ>NN−1.
For f∈L1(Ω), we have the following theorem.
Theorem 1.5. Assume (1.2)-(1.3) hold, f∈L1(Ω) is a nonnegative function and θ=NN−1. Then there exists a distributional solution u∈W1,10(Ω) to problem (1.1).
The paper is organized as follows. In section 2, we collect some definitions and useful tools. The proof of Theorem 1.2 and 1.5 be given in section 3.
In order to prove our main results, we need to introduce a basic definition and some lemmas.
Definition 2.1. For all k≥0, the truncation function defined by
Tk(s)=max{−k,min{k,s}},Gk(s)=s−Tk(s). |
Let 0<ε<1, we approximate problem (1.1) by the following non-singular problem
{−div(M(x,T1ε(uε))∇uε)+uε|∇uε|2(|uε|+ε)θ+1=fε,x∈Ω,uε=0,x∈∂Ω, | (2.1) |
where fε=T1ε(f). Problem (2.1) admits at least a solution uε∈H10(Ω)∩L∞(Ω) by Theorem 2 of [8]. Due to the fact that fε≥0 and quadratic lower order term has the same sign of the solution, it is easy to prove that uε≥0 by taking u−ε as a test function in (2.1).
Lemma 2.2. Let uε be the solutions to problem (2.1). Then
∫Ωuε|∇uε|2(uε+ε)θ+1≤∫Ωf. | (2.2) |
Proof. For fixed h>0, taking Th(uε)h as a test function in (2.1). Dropping the first term, we obtain
∫Ωuε|∇uε|2(uε+ε)θ+1Th(uε)h≤∫ΩfεTh(uε)h. |
Using the fact that fε≤f and Th(uε)h≤1, then
∫Ωuε|∇uε|2(uε+ε)θ+1Th(uε)h≤∫Ωf. |
Letting h→0, we deduce (2.2) by the Fatou Lemma.
Lemma 2.3. Let δ>0 and 0<ε<1. Then there exists C>0, such that
αδ(t+ε)θ−2(ρ+t)γ+tt+ε≥C. |
for every t≥0.
Proof. Clearly, if t≥ε, we have tt+ε≥12, while if t<ε, we have
αδ(t+ε)θ−2(ρ+t)γ≥αδ(ρ+t)γ(2ε)2−θ≥αδ22−θ(ρ+1)γ, |
since ε<1. Therefore, Lemma 2.3 is proved.
In this section, C denotes a generic constant whose value might change from line to line. We prove the existence results of Theorems 1.2 and 1.5 by considering the following approximate problem
{−div(M(x,T1ε(uε))∇uε)+uε|∇uε|2(uε+ε)θ+1=fε,x∈Ω,uε=0,x∈∂Ω. | (3.1) |
Proof of Theorem 1.2. Step 1: Let δ=θ−NN−1, then δ>0 by (1.6). Choosing (uε+ε)δ−(uε+ε)δ−1 as a test function in the approximate problem (3.1), we find
∫ΩM(x,T1ε(uε))∇uε⋅∇uε[δ(uε+ε)δ−1+(1−δ)(uε+ε)δ−2]+∫Ωuε(uε+ε)δ|∇uε|2(uε+ε)θ+1=∫Ωuε|∇uε|2(uε+ε)θ+1(uε+ε)δ−1+∫Ωfε[(uε+ε)δ−(uε+ε)δ−1]. |
Combining (1.2)-(1.3) and dropping the positive term, we obtain
∫Ω|∇uε|2(uε+ε)δ−θ[α(1−δ)(uε+ε)θ−2(ρ+uε)γ+uεuε+ε]≤∫Ωuε|∇uε|2(uε+ε)θ+1(uε+ε)δ−1+∫Ωfε(uε+ε)δ. |
Since 1−δ>0, according to Lemma 2.3, we have
C∫Ω|∇uε|2(uε+ε)δ−θ≤∫Ωuε|∇uε|2(uε+ε)θ+1(uε+ε)δ−1+∫Ωfε(uε+ε)δ. |
Using the fact that uε≥0,fε≤f and (2.2), we obtain
C∫Ω|∇uε|2(uε+ε)δ−θ≤εδ−1∫Ωuε|∇uε|2(uε+ε)θ+1+∫Ωf(uε+ε)δ≤εδ−1∫Ωf+∫Ωf(uε+ε)δ. | (3.2) |
Observe that the left hand side of (3.2) can be rewritten as
C∫Ω|∇[(uε+ε)δ−θ+22−εδ−θ+22]|2. | (3.3) |
Then, (3.2) and (3.3) imply
C∫Ω|∇[(uε+ε)δ−θ+22−εδ−θ+22]|2≤εδ−1∫Ωf+∫Ωf(uε+ε)δ. | (3.4) |
By the Sobolev inequality, satisfy
[∫Ω|(uε+ε)δ−θ+22−εδ−θ+22|2∗]22∗≤C∫Ω|∇[(uε+ε)δ−θ+22−εδ−θ+22]|2. | (3.5) |
Using the Hölder inequality and (3.4)-(3.5), we get
[∫Ω|(uε+ε)δ−θ+22−εδ−θ+22|2∗]22∗≤C‖f‖Lm(Ω)+C‖f‖Lm(Ω)[∫Ω(uε+ε)δm′]1m′. |
Since |(t+ε)s−εs|2∗≥C[(t+ε)2∗s−1] for every t≥0 and for suitable constant C independent on ε, then we find
(∫Ω[(uε+ε)2∗(δ−θ+2)2−1])22∗≤C‖f‖Lm(Ω)+C‖f‖Lm(Ω)[∫Ω(uε+ε)δm′]1m′. | (3.6) |
Thanks to the choice of δ, we have
2∗(δ−θ+2)2=δm′=NN−1. |
Moreover 22∗>1m′ since m<N2. Then (3.6) implies that
∫ΩuNN−1ε≤C. | (3.7) |
Observe that δ−θ=−NN−1, then, (3.2), (3.7) follow
∫Ω|∇uε|2(ε+uε)NN−1≤C. | (3.8) |
Combining (3.7)-(3.8) with the Hölder inequality, we obtain
∫Ω|∇uε|=∫Ω∇uε(ε+uε)N2N−2(ε+uε)N2N−2≤[∫Ω|∇uε|2(ε+uε)NN−1]12[∫Ω(ε+uε)NN−1]12≤C. |
Then we get that {uε} is bounded in W1,10(Ω). Hence, there exists a subsequence {uε}, which converges to a measurable function u a.e. in Lr(Ω) with 1≤r<NN−1.
Step 2: First, we are going to estimate ∫{uε≥k}|∇uε|. Choosing [(uε+ε)δ−(k+ε)δ]+ as a test function in (3.1). By (1.2)-(1.3) and Lemma 2.3, we have
∫{uε≥k}|∇uε|2(ε+uε)NN−1≤(∫{uε≥k}|f|m)1m(∫{uε≥k}(ε+uε)NN−1)1m′≤C(∫{uε≥k}|f|m)1m. |
Using the Hölder inequality and (3.7), we find
∫{uε≥k}|∇uε|=∫{uε≥k}∇uε(ε+uε)N2N−2(ε+uε)N2N−2≤C(∫{uε≥k}|f|m)12m. | (3.9) |
Choosing Tk(uε) as a test function in (3.1). Dropping the nonnegative lower order term, by (1.2)-(1.3) and the boundedness of uε in LNN−1(Ω), we get
∫Ω|∇Tk(uε)|2≤k(ρ+k)γα‖f‖L1(Ω). | (3.10) |
This implies that Tk(uε)⇀Tk(u) weakly in W1,20(Ω).
Let E be a measurable subset of Ω, and i=1,⋯,N. By the Hölder inequality and (3.9)-(3.10), we obtain
∫E|∂uε∂xi|≤∫E|∇uε|≤∫E|∇Tk(uε)|+∫{uε≥k}|∇uε|≤meas(E)12(∫E|∇Tk(uε)|2)12+C(∫{uε≥k}|f|m)12m. | (3.11) |
The estimates (3.7) and (3.11) shows that the sequence {∂uε∂xi} is equi-integrable. Thus, by the Dunford–Pettis theorem, there exists a subsequence {uε} and Vi in L1(Ω), such that ∂un∂xi⇀Vi in L1(Ω). Since ∂uε∂xi is the distributional partial derivative of uε, then we have
∫Ω∂uε∂xiφ=−∫Ωuε∂φ∂xi,∀φ∈C∞0(Ω), |
for every ε>0.
Since ∂uε∂xi⇀Vi in L1(Ω) and uε→u in L1(Ω), we find
∫ΩViφ=−∫Ωu∂φ∂xi,∀φ∈C∞0(Ω). |
This implies that Vi=∂u∂xi for every i.
Step 3: We prove that uε|∇uε|2(uε+ε)θ+1 is equi-integrable. Let E⊂⊂Ω, then
∫Euε|∇uε|2(uε+ε)θ+1≤∫E∩{uε≤k}uε|∇uε|2(uε+ε)θ+1+∫E∩{uε≥k}uε|∇uε|2(uε+ε)θ+1. |
For every subset E⊂⊂Ω,
∫E∩{uε≤k}uε|∇uε|2(uε+ε)θ+1≤∫E∩{uε≤k}1uθε|∇Tk(uε)|2≤C∫E∩{uε≤k}|∇Tk(uε)|2, |
since uε≥C>0 in E by Proposition 2 of [9]. Moreover, since Tk(uε)⇀Tk(u) weakly in W1,20(Ω), then there exists εn,δ>0, such that
∫E∩{uε≤k}|∇Tk(uε)|dx≤ϵ2,∀ε≥εn, | (3.12) |
for every ϵ>0 if μ(E)<δ.
Choosing T1(uε−Tk−1(uε)) as a test function in the approximate problem (3.1), dropping the nonnegative term, we have
∫{uε≥k}uε|∇uε|2(uε+ε)θ+1≤∫{uε≥k−1}f. | (3.13) |
Observe there exists a constant C>0, such that μ(uε≥k−1)≤Ck−1. As uε are uniformly bounded in LNN−1(Ω). This implies the right hand side of (3.13) converges to 0 as k→∞. Thus, we deduce there exists k0>1, such that
∫{uε≥k}uε|∇uε|2(uε+ε)θ+1≤ϵ2,∀k>k0, | (3.14) |
for every ϵ>0. The (3.12), (3.14) imply that uε|∇uε|2(uε+ε)θ+1 is equi-integrable and converges a.e. to |∇u|2uθ.
Let u the weak limit of the sequence of approximated solutions uε. Thanks to (2.2), we have
∫Ωuε|∇uε|2(uε+ε)θ+1≤∫Ωf. |
Using the Fatou lemma, that uε convergence to u a.e, ∇uε convergence to ∇u a.e and the strict positivity of uε imply
∫Ω|∇u|2uθ≤∫Ωf≤C. |
This show that |∇u|2uθ∈L1(Ω).
Since uε is bounded and ∇uε→∇u a.e, it follow M(x,T1ε(uε)∇uε→M(x,u)∇u a.e. Hence, we can pass to the limit in (3.1). Thus prove that u∈W1,10(Ω) is a distributional solution of (1.1) and yields the conclusion of the proof of Theorem 1.1.
Proof of Theorem 1.5. Step 1: For 0<ε<1, according to Lemma 2.2, we have
12θ+1∫{uε≥1}|∇uε|2uθε≤∫{uε≥1}uε|∇uε|2(uε+ε)θ+1≤‖f‖L1(Ω). | (3.15) |
By the Sobolev inequality, (3.15) lead to
[∫Ω|u2−θ2ε−1|2∗]22∗≤C‖f‖L1(Ω), | (3.16) |
which implies that
[∫Ωu(2−θ)2∗2ε]22∗≤C+C‖f‖L1(Ω). | (3.17) |
Observe that θ=(2−θ)2∗2=NN−1. Then (3.17) shows that
∫ΩuNN−1ε≤C. | (3.18) |
Using the Hölder inequality and (3.15), (3.18), we obtain
∫Ω|∇G1(uε)|=∫{uε≥1}|∇G1(uε)|uθ2εuθ2ε≤[∫{uε≥1}|∇uε|2uθε]12[∫{uε≥1}uθε]12≤C‖f‖L1(Ω). |
This fact show that G1(uε) is bounded in W1,10(Ω).
Choosing T1(uε) as a test function in (3.1), it is easy to prove that T1(uε) is bounded in H10(Ω)), hence in W1,10(Ω). Since uε=G1(uε)+T1(uε), we deduce that uε is bounded in W1,10(Ω).
Moreover, due to (3.15) and the Hölder inequality, we have
∫{uε≥k}|∇uε|=∫{uε≥k}|∇uε|uθ2εuθ2ε≤C‖f‖12L1(Ω). | (3.19) |
That (3.10), (3.19) implies, for every measurable subset E, we have
∫E|∂uε∂xi|≤∫E|∇uε|≤∫E|∇Tk(uε)|+∫{uε≥k}|∇uε|≤meas(E)12[k(ρ+k)γα‖f‖L1(Ω)]12+C‖f‖12L1(Ω). |
Thus, we prove that uε⇀u in W1,10(Ω). Then pass to the limit in problem (3.1), as in the proof of Theorem 1.2, it is sufficient to observe that u∈W1,10(Ω) is a distributional solution of (1.1). This concludes the proof the Theorem 1.5.
In this paper, we main consider the existence of W1,10(Ω) solutions to a elliptic equation with principal part having noncoercivity. The main results show that the singular quadratic term has an important impact on this existence.
This research was partially supported by the National Natural Science Foundation of China (No. 11761059), Program for Yong Talent of State Ethnic Affairs Commission of China (No. XBMU-2019-AB-34), Fundamental Research Funds for the Central Universities (No.31920200036) and First-rate Discipline of Northwest Minzu University.
The authors declare that there is no conflict of interests regarding the publication of this article.
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