Bull. Hiroshima Inst. Tech. Research Vol.48（2014）93-103

ArticleA Novel Approach of Image Enhancement using Steganographic Data
Embedding and JPEG Scheme

Isao FURUKAWA* and Junji SUZUKI**（Received Oct. 30, 2013）

Abstract
We propose a new image enhancement method for JPEG images on the Internet that a sender who
has high resolution images embeds enhancement codes for representing high frequency DCT coefﬁcients into low frequency DCT coefﬁcients, and receivers recover either low resolution JPEG coded
images by using conventional JPEG decoder or high resolution images by using code extraction
from low frequency DCT coefﬁcients. This image transmission method can be thought as so calledsteganographic communication in case that these enhancement codes are kept secret. In order toimplement the steganographic image communication, after the DCT size is extended from 8×8 to
16 × 16, SNRs are measured for images not only with setting all high frequency coefﬁcients greaterthan 8 × 8 size to zeros, then processing IDCT of 16×16 size, but also with processing IDCT of 8×8
size. A scanning method for high frequency DCT coefﬁcients is also proposed. The simulation experiments show the amount of enhancement codes for recovering quantized high frequency DCT
coefﬁcients and SNRs without embedding enhancement codes into low frequency DCT coefﬁcients.
Key Words: image communication, steganography, image enhancement, JPEG compression, DCT,

entropy coding

high frequency components are estimated by Bayesian estimation using multiple image frames of thesame scene [ 3]. As mentioned above, conventional
image enhancement technologies such as SR only
estimate missing signals, so that these have restricted
properties that rely on edge enhancement, nonlinear
filtering, spline functional interpolation, and so on
when a single image frame is available, though thesehave some advantages when several images areavailable.
A basic idea of communications with steganographic information-embedded image enhancement
scheme is that the high frequency components areembedded in the low frequency components in theencoder, and then the embedded components are utilized to recover enhanced images in the decoder. For

1. INTRODUCTION

A high resolution image can be obtained by adding
high frequency components to a low resolution image with some restricted bandwidth. On the other
hand, an image enhancement method called superresolution (SR) is also used, if the high frequency
components are estimated from only low resolution
images without using high frequency signal information [1]. This problem is essentially ill-posed one
and the precise recovering is impossible [2], because
the estimated high frequency components are beyond
the maximum frequency that is restricted by the sampling theorem. A reasonable reconstruction of thehigh frequency components needs a priori knowledge about the original image. For example, missing＊＊＊＊＊＊

Department of Computer Science, Faculty of Applied Information Science, Hiroshima Institute of Technology
School of Information Science and Technology, Aichi Prefectural University― 93 ―

Isao FURUKAWA and Junji SUZUKI

example, an steganographic image compression
method that is premised on using JPEG as the underlying image coding method enables the parties who
know the secret can recover high resolution images
exclusively, and the public people who do not know
the secret can only recover low resolution images using a conventional JPEG decoder. This means thatthe steganographic image compression method havea compatibility with the existing systems, if the output bit stream of the encoder is conforming to the
JPEG standard. Moreover it may not seem to be acovert communication at a first glance. Of course,
there are many steganalysis methods that investigate
the outlier of the encoder output codes from the normal JPEG encoder bit stream, but it is not often done
and may not be suspicious.
Another application is for a business model thathigh resolution images, such as 4K, can be incorporated in the existing HDTV broadcasting system byembedding the high frequency components in the
low frequency signal. In this scenario, users who
have 4K TV can view 4K image contents with additional payment to extract and decrypt the embedded
components.

In order to implement the steganographic image
enhancement system, we have to study the frequency
characteristics of DCT viewed as a ﬁlter bank, embedding method of high frequency component into
low frequency signal, and human visual sensitivity
against quality degradation by information embedding. This paper discusses basic experimental results
toward the implementation of the steganographic image enhancement communication. First of all, a fundamental framework to design steganographic information embedding method based on the JPEG
standard is described in Section 2. The simulation
conditions for the experiments are presented in Section 3. The simulation results are demonstrated in
Section 4, and conclusions are given in Section 5.
2. BASIC CONFIGURATONS
2.1 Encoder and decoder systems

This system assumes a couple of special encoder anddecoder, and conventional decoders placed on theInternet. These encoder and decoders have the following functions.
(a) Special encoder: Original images with 2N×2N
pixels are ﬁrst transformed by 16×16 DCT, andSpecial JPEG encoder

Original image㻌Standard JPEG decoder

Low-resolution image㻌

8×8 IDCT as
specified in JPEG
standard

8×8
DCT
coefficientsInternet

Special JPEG decoder

Embedding
8×8㻌
DCT㻌
coefficients 㻌

8×8㻌
DCT㻌
coefficients 㻌

Recovered highresolution image㻌16
16×16㻌DCT
coefficients

㻌

Place

Extraction

8×8㻌
DCT㻌
coefficients 㻌

16㻌

16
16×16㻌DCT㻌㻌
coefficients
16
Fig. 1 Notion of image enhancement with JPEG and steganographic data embedding.

― 94 ―

Y
R
G
B

Color transform

Original high-res. image

A Novel Approach of Image Enhancement using Steganographic Data Embedding and JPEG Scheme

U

16×16
DCT

8×8 coeff.
extraction

Quantization

LSB
embeddingentropy encoding㻌file.jpg

QF1

V
16×16 coeff.
extraction(except 8×8)

Quantization㻌㻌

entropy encoding

QF2(a) Steganographic JPEG encoder

Y
U

QF1

㻌 LSB
extraction㻌
entropy decoding

16×16
DCT coeff.
recovering

Inversequantization
QF2(b) Standard JPEG and steganographic JPEG decoders

V

16×16
IDCT

Y
U
V

R

R

G
B

G
B

Low-res. image㻌

8×8
IDCT

High-res. image㻌

Inversequantization

entropy decoding

Color transform㻌

file.jpg

Color transform㻌

Standard JPEG decoder

Fig. 2 Conﬁgurations of steganographic JPEG encoder/decoder and standard JPEG decoder.

then the codes for the quantized high frequency
DCT coefﬁcients other than 8×8 coefﬁcients areembedded in the LSBs of 8×8 quantized low
frequency DCT coefﬁcients. The 8×8 DCT coefﬁcients are encoded using conventional JPEG
entropy encoder. The output of the special encoder is conformed to the JPEG standard.
(b) Standard decoder: It receives a JPEG bit stream
embedded high frequency components in 8×8
DCT coefﬁcients, and decodes it to obtain a decoded image with N×N-pixel resolution by conventional JPEG decoding procedure.
(c) Special decoder: It receives a JPEG bit stream
embedded high frequency components in 8×8
DCT coefﬁcients, and extract the quantized highfrequency DCT coefficients to obtain 16×16
DCT coefficients, then a 2N×2N-pixel resolution images are recovered by 16×16 IDCT.
One example for the encoder and the decoders＇arrangement is shown in Figure 1. The special encoder described above outputs a bit stream that hasapparently different characteristics from that of theconventional JPEG encoder (e.g., the ﬁle size fromthe special encoder will be larger than that from theconventional JPEG encoder), so that the steganographic embedding may be identiﬁed using various
statistical analysis. If the image content is not sovaluable, no security guard will be necessary. Otherwise, it can be protected using secure methods suchas encryption.
Figure 2 shows the conﬁgurations of the steganographic JPEG encoder/decoder and the standard
JPEG decoder to implement the functions depicted
in Figure 1. The individual functions are described
below.
2.2 DCT/IDCT as ﬁlter bank system
2.2.1 N-point DCT and N-point IDCTLet us consider a frequency characteristic of one dimensional DCT/IDCT system that the N-point IDCT
is performed after setting zeros of the higher frequency coefﬁcients other than lower L-point coefﬁcients of the N-point DCT. This DCT/IDCT system
is shown in Figure 3. The output of the system, y(n),that the output of the N-point DCT, X(n), is restricted
up to lower L points can be shown as follows by N― 95 ―

Isao FURUKAWA and Junji SUZUKI

point DCT/IDCT formula, which is same as thatused in the JPEG standard.
2 N −1(2m + 1)kπ
(2n + 1)kπ 1  , L −1− 
y (n) = ∑ x(m)  ∑ cos
cos
N m=0
N
2
2N
2
 k =0H1 ( z ) = h0,0 + h0,1 z1 + L + h0, N −1 z N −1 ,−1H 2 ( z ) = h1,0 z + h1,1 + L + h1, N −1 z

(1)

2  L −1(2m + 1)kπ
(2n + 1)kπ 1 ∑ cos 2 N cos 2 N − 2  ,N  k = 0

H N −1 ( z ) = hN −1,0 z − ( N −1) + hN −1,1 z − ( N − 2) + L + hN −1, N −1 .

N −1

H n ( z ) = ∑ hn , m z m − n , n = 0, L , N − 1.
m=0

and we get the following relationship from Eq. (1).
y (n) = ∑ hn , m x( m), n = 0, L, N − 1.

Actually, Eq. (3) is constructed with N equations
concerning with y(n), n = 0, ..., N-1.
y (0) = h0,0 x(0) + h0,1 x(1) + L + h0, N −1 x( N − 1),y (1) = h1,0 x(0) + h1,1 x(1) + L + h1, N −1 x( N − 1),Ly ( N − 1) = hN −1,0 x(0) + hN −1,1 x(1) + L + hN −1, N −1 x( N − 1).









(4)

This set of equations can be seen as the following N
FIR digital ﬁlters.

y(0)

x(1)

X(1)

y(1)

…
x(L−1)…
x(N−1)…

…

X(L−1)…

all
X(N−1) zero

N-point IDCTX(0)N-point DCT

x(0)

…

H ( z) =…

…
y(N−1)

Fig. 3 N-point DCT/N-point IDCT system with only L-point

coefﬁcients.

y2 (n) = h1,0 x(n − 1) + h1,1 x(n) + L + h1, N −1 x(n + N − 2),L
y N −1 (n) = hN −1,0 x(n − N + 1) + hN −1,1 x(n − N + 2) + L + hN −1, N −1 x(n).

(8)

1 N −1∑ H n ( z) .
N n=0

(9)

It is obvious that the overall transfer function isequal to 1 in case of L = N.
Figure 4 shows ¦Hn(z)¦ (n = 0, 1, ..., 15) and ¦H(z)¦with N = 16 and L = 8, while Figure 5 shows ¦H(z)¦with L = 8, 9, ..., 16. These results show that the ﬁlter bank system has a low pass characteristic, and theattenuation magnitude of the stop band frequency isabout 15 (dB).

y(L−1)y1 (n) = h0,0 x(n) + h0,1 x( n + 1) + L + h0, N −1 x(n + N − 1),(7)

The outputs of the FIR digital filter would notbeen seen as the summed result of each digital ﬁlters
when they are constructed in parallel, because the ﬁnal output signals of the system are not summed toobtain y(n) (i.e., N pieces of y(n) signals themselves
are the output of the system). Therefore, H(z) ≠ ΣnHn(z), where H(z) is an overall transfer function of
the filter bank system. We assume instead that the
expected transfer function is obtained when the outputs of these N digital ﬁlters are observed as an average. That is, we expect the spectrum amplitude of
the overall transfer function |H(z)| as follows, which
is derived from the digital ﬁlters of N-point DCT/Npoint IDCT system.

(3)

m=0









As a result, we get the transfer function of the ﬁlter
bank system as follows.

(2)

N −1

,

L

where n is an integer, n = 0, 1, ..., N-1. Then, as the
coefﬁcient of x(m) is a constant if n and m are determined, it is written byhn , m =

N −2









2.2.2 N-point DCT and L-point IDCT (L ≤ N)

(5)

Let us consider a frequency characteristic of one dimensional DCT/IDCT system that the L-point IDCT
(L ≤ N) is performed after discarding the higher frequency coefﬁcients other than lower L-point coefﬁcients of the N-point DCT. This DCT/IDCT system
is shown in Figure 6. In this case, the gain of theoutput signal, y(n), resulted from the L-point IDCT is――
L/N
multiplied by √―, because the order of the basecosine function is changed. Therefore, the output of
the system, y(n), that the higher frequency coefficients of the N-point DCT, X(n), are discarded can be

By Z-transforming, we get
Y1 ( z ) = ( h0,0 + h0,1 z1 + L + h0, N −1 z N −1 ) X ( z ),Y2 ( z ) = ( h1,0 z −1 + h1,1 + L + h1, N −1 z N − 2 ) X ( z ),L
YN −1 ( z ) = ( hN −1,0 z − ( N −1) + hN −1,1 z − ( N − 2) + L + hN −1, N −1 ) X ( z ), (6)
therefore, if we define Hn(z)=Yn(z)/X(z), n = 0, ...,
N-1, as a transfer function of the ﬁlter bank system,
this can be written as― 96 ―

A Novel Approach of Image Enhancement using Steganographic Data Embedding and JPEG Scheme

shown as follows by N-point DCT/L-point IDCT formula, which is same as that used in the JPEG standard.
2 N −1(2m + 1)kπ
(2n + 1)kπ 1  L −1− .
y (n) = ∑ x(m)  ∑ cos
cos
2
2L
2
N m=0
N
 k =0G1 ( z ) = g 0,0 + g 0,1 z1 + L + g 0, N −1 z N −1 ,
−1G2 ( z ) = g1,0 z + g1,1 + L + g1, N −1 z

2  L −1(2m + 1)kπ
(2n + 1)kπ 1 ∑ cos 2 N cos 2 L − 2  ,N  k = 0(10)

GL −1 ( z ) = g L −1,0 z − ( N −1) + g L −1,1 z − ( N − 2) + L + g L −1, N −1 .

N −1Gn ( z ) = ∑ g n , m z m − n , n = 0, L , L − 1.
m=0

and we get the following relationship from Eq. (10).
y (n) = ∑ g n , m x(m), n = 0, L, L − 1.

Actually, Eq. (12) is constructed with N equations
concerning with y(n), n = 0, ..., N-1.
y (0) = g 0,0 x(0) + g 0,1 x(1) + L + g 0, N −1 x( N − 1),y (1) = g1,0 x(0) + g1,1 x(1) + L + g1, N −1 x( N − 1),
L
y ( L − 1) = g L −1,0 x(0) + g L −1,1 x(1) + L + g L −1, N −1 x( N − 1).









G( z) =(13)

y1 (n) = g 0,0 x(n) + g 0,1 x(n + 1) + L + g 0, N −1 x(n + N − 1),
L
yL −1 (n) = g L −1,0 x(n − N + 1) + g L −1,1 x(n − N + 2) + L + g L −1, N −1 x(n).









x(1)

X(1)

…
x(L−1)…

N-point DCT

X(0)x(N−1)…

…

X(L−1)

L-point IDCT

(14)
x(0)

its code representationy(1)The special encoder is divided into two parts:
one is for encoding 8×8 DCT coefﬁcients within 16
×16 DCT coefﬁcients，the other is for encoding 16
×16 DCT coefficients other than lower 8×8 DCT
coefﬁcients. For extracting the latter higher frequency DCT coefﬁcients, we employ a scanning methodwith inverse L-shaped pattern as shown in Figure 9.
In this case, the numbers of scanned quantized DCT
coefﬁcients are,ﬁrst scan: 9 + 9 －1 = 17 coefﬁcients,
second scan: 10 + 10 －1 = 19 coefﬁcients,
third scan: 11 + 11 －1 = 21 coefﬁcients,…
y(L−1)

discardFig. 6 N-point DCT/L-point IDCT system with only L-point

coefﬁcients.

By Z-transforming, we get
Y1 ( z ) = ( g 0,0 + g 0,1 z1 + L + g 0, N −1 z N −1 ) X ( z ),Y2 ( z ) = ( g1,0 z + g1,1 + L + g1, N −1 z
−1

N −2

) X ( z ),L
YL −1 ( z ) = ( g L −1,0 z − ( N −1) + g L −1,1 z − ( N − 2) + L + g L −1, N −1 ) X ( z ).

(18)

2.3 Scanning of higher frequency DCT coefﬁcients andy(0)…
X(N−1)

1 L −1∑ Gn ( z ) .
L n=0

It is obvious that the overall transfer function isequal to 1 in case of L = N.
Figure 7 shows |Gn(z)| (n = 0, 1, ..., 7) and |G(z)|with N = 16 and L = 8, while Figure 8 shows |G(z)|with L = 8, 9, ..., 16. These results show that the ﬁlter
bank system has a low pass characteristic, and the attenuation magnitude of the stop band frequency isaround 10 to 15 (dB).

This set of equations can be seen as the following N
FIR digital ﬁlters.
y2 (n) = g1,0 x(n − 1) + g1,1 x(n) + L + g1, N −1 x(n + N − 2),(17)

If we assume that the expected transfer function
is obtained when the outputs of these N digital ﬁlters
are observed as an average as discussed in 2.2.1, thetransfer function | G ( z )| is expressed as follows,which is derived from the digital filters of N-point
DCT/L-point IDCT system.

(12)

m=0




 (16)


As a result, we get the transfer function of the ﬁlter
bank system as follows.

(11)

N −1

,

L

As discussed in 2.2.1, the coefﬁcient of x(m) is a constant if n and m are determined, and it is written by
g n,m =

N −2









…
8-th scan: 16 + 16 －1 = 31 coefﬁcients,(15)
therefore, if we deﬁne Gn(z) = Yn(z)/X(z), n = 0, ..., N-1, as a transfer function of the filter bank system,
this can be written as

hence, the total number of scanned coefﬁcients is 17+ 19 + 21 ... + 31 = 192. The encoded data with runlength Huffman encoding method are embedded in 8
×8 ＝ 64 lower frequency DCT coefﬁcients.
― 97 ―

Isao FURUKAWA and Junji SUZUKI

scan=2㻌 scan=8㻌㻌88 DCT㻌
coefficients16㻌㻌

scan=1component in such a case, and these codes give minimum code length. Of course, the amount of the enhancement codes depends on the quality factor forthe higher frequency DCT coefﬁcients as describedabove.

scan=3㻌

3. EXPERIMENT CONDITIONS㻌

16㻌

Now, we explain the subjects that should be clariﬁedby the simulation experiments.
(1) The quality factor for the 8×8 DCT coefﬁcientsthat makes the quantization noise invisible when
8 × 8 DCT coefﬁcients are inverse transformed
with 16 × 16 IDCT.
(2) As discussed in 2.2.1 and 2.2.2, DCT/IDCT system can be viewed as a ﬁlter bank and the attenuation magnitude in the stop band frequency isaround 10 to 15 (dB). This attenuation magnitude may be too small to make the ringing artifacts invisible. In order to prevent the artifacts,
we employ two approaches: The ﬁrst is to clarify
the number of scans to prevent the ringing artifacts when the number of scans of the higher
DCT coefﬁcients becomes 9×9, 10×10, ..., 16×
16 (in this case, the parameter is the quality factor for lower frequency DCT coefﬁcient quantization). The second is to clarify the quality factor to prevent the ringing artifact when all the 16
× 16 DCT coefﬁcients are used (in this case, theparameter is the quality factor for the higher frequency DCT coefﬁcient quantization).
(3) In case of (2), how much the amount of the enhancement codes compared with the resulting
JPEG entropy codes is, and how much the rate
described above decreases when the quality factor of the lower frequency DCT coefﬁcients increases. In other words, what is the characteristic of the payload for the enhancement codes?
(4) Do the results from (1) through (3) change withdifferent image contents and the pixel resolutionof original images?
In order to consider the above mentioned subjects, we divide the experiment into three phases, i.e.,phases I to III. Note that the actual embedding of theenhancement codes into the lower frequency DCT
coefﬁcients is not implemented, because we treat it
as a further study in conjunction with a subjective

Fig 9. Scan pattern for higher frequency DCT coefﬁcients

As shown in Figure 2, it is possible to definethat the quality factors (QFs) for lower 8×8 DCT
coefﬁcients and higher 16×16 DCT coefﬁcients other than 8 × 8 have different values of QF1 and QF2,
respectively. The quantization step size Ai is computed as follows using the quality factor, QF ∈ {1, 2,..., 99} when the default quantization step size is Bi.
 B ⋅ S + 50 
Ai =  i, 100 

(19)

where
200 − 2QF
S =5000 / QF

for QF ≥ 50,
for QF ≤ 50.

(20)

If QF = 50, then the quantization is set as the default
value. The quality factors of QF > 50 and QF < 50give high quality quantization (low compressionrate) and low quality quantization (high compressionrate), respectively. For example,
・if QF = 50,1 B ⋅ 100 + 50  
Ai =  i =  Bi + 2  = Bi ,
100


 

・else if 99 ≥ QF > 50,
QF  1  B ⋅ (200 − 2QF ) + 50   
Ai =  i =  Bi  2 − 50  + 2  ,
100


  


・else if 1 ≤ QF < 50, B ⋅ (5000 QF ) + 50   50 1 
Ai =  i =  Bi QF + 2  .
100

 


In order to make the encoding of the enhancement data easy, we use default run-length Huffman
codes for zigzag scanning of 8×8 quantized DCT
coefﬁcients for luminance and chrominance components. Here, if all the coefﬁcients in the higher frequency domain is zero, then a speciﬁc code of EOB(end of block) is assigned to express the state, so thatthe enhancement code is 4-bit“1010”for the luminance component and 2-bit“00”for the chrominance― 98 ―

A Novel Approach of Image Enhancement using Steganographic Data Embedding and JPEG Scheme

test. The experiment phases I to III are as follows:
I. The SNRs for which reduced-size JPEG coded
image and original-size JPEG coded image without image enhancement (i.e., scan = 0) are measured with various quality factors of the lower
frequency DCT coefﬁcient quantization. These
SNRs include three measurements: SNRlowres thatis computed using reduced-size JPEG coded image and 16 × 16 DCT/8 × 8 IDCT image as a reference, SNRlowpass that is computed using original-size JPEG coded image with scan = 0 andoriginal high resolution image as a reference,and SNR that is computed using only low-passed
original-size 16×16 DCT/ 16×16 IDCT withzero higher frequency and original high resolution image as a reference. The compression ratioof the reduced-size JPEG coding is also measured. These measurements are applied to thethree chrominance formats of 4:4:4, 4:2:2, and4:1:1. A simple subjective test is performed toobtain a guideline needed for the quality factorof the lower frequency DCT coefﬁcients in caseof scan = 0 (the quality factors, QF1, are selected
as 40, 45, 50, 55, ..., 90, 95 with step of 5).
II. In addition to the conditions of experiment phase
I, the quality factor, QF1, needed to make the artifacts invisible should be confirmed when theenhancement codes are used to recover the original-size JPEG encoded image. In order to obtaina fundamental property, the amount of enhancement codes is measured with various combinations of the quality factor of the higher frequency
DCT coefﬁcients, QF2, and the number of scans.
More precisely, An average, maximum, andminimum amount of enhancement codes per
block (16 × 16 region) for a combinations of scan
= 1, 2, ..., 8 and QF2 ＝ 40, 45, 50, 55, ..., 90, 95
with step of 5. In this experiment, QF1 is irrelevant to the results
III. The steganographic data embedding is equivalent to adding quantization distortion to 8×8
DCT coefﬁcients. Therefore, an experiment thatinvestigates a ratio of the amount of enhancement codes to that of JPEG bit stream gives afundamental property of the steganographic data
embedding system when the quality factors ofboth lower and higher frequency DCT coefficients are changed. The ratio of the amount ofenhancement codes to JPEG bit stream is measured using a combination of QF1, QF2 ＝ 40, 45,
50, 55, ..., 90, 95 with step of 5.
Six test images used in the experiment is described in Table 1 and Figure 10. L1, L2, and L3 arecomparatively low resolution images, while H1, H2,
H3 are comparatively high resolution images. The
image L1 is SIDBA image, and the images L2 andL3 are obtained from [1] and clipped away to obtaincenter regions. The images H1, H2, and H3 are allfrom [2] and clipped away to obtain 2048×2048 region.
Table 1 Test images.

Name
L1
L2
L3
H1
H2
H3

Pixel resolution
W512 × H512
W672 × H544
W672 × H544
W2048 × H2048
W2048 × H2048
W2048 × H2048

Note
lena
barbaragoldhill
p1rgb in SHIPP
p2rgb in SHIPP
p3rgb in SHIPP

4. EXPERIMENT RESULTS
4.1 Experiment phase I• Experiment conditions
1. No scanning is performed for higher frequency
DCT coefﬁcients (i.e., scan = 0).
2. The chrominance formats are 4 : 4 : 4 , 4 : 2 : 2 , and4:1:1.
3. 12 QF1 are selected as 40, 45, ..., 90, 95 with stepof 5.
• Measured items
1. SNRlowpass that is measured as a SNR of low-passﬁltered original-size JPEG coded image (scan = 0)
for various QF1 values against original pixel resolution image.
2. SNRlowres that is measured as a SNR of reducedsize JPEG coded image for various QF1 values
against the output of 16×16 DCT/8×8 IDCT ﬁlter
bank system.
3. Compression ratio of reduced-size JPEG encoded
image.
• Measured results
The measured results of SNRlowpass and SNRlowres areshown in Figures 11 and 12. The compression ratios― 99 ―

Isao FURUKAWA and Junji SUZUKI

for images L1, L2, and L3 are lower than that for images H1, H2, and H3. These results mean that the images L1, L2, and L3 have more high frequency components than the images H1, H2, and H3.
Speciﬁcally, as shown in Figure 11, images L1, L2,and L3 have low SNRlowpass, so that the large aliasing
effect is expected by the low pass ﬁltering with 16×
16-point DCT/8 × 8-point IDCT system. On the other
hand, images H1, H2, and H3 have comparatively
small high frequency components, and, of these, thehigh frequency component of image H1 is extremely
low. A simple subjective experiment shows thatsome image degradations are detected for L1 with
QF1 < 90 when the image is magniﬁed on a 23-inch
HD LCD monitor.
4.2 Experiment phase II• Experiment conditions
1. 8 scanning modes are employed (scan = 1, 2, ..., 8).
2. The chrominance formats are 4 : 4 : 4 , 4 : 2 : 2 , and4:1:1.
3. 12 QF2 are selected as 40, 45, ..., 90, 95 with stepof 5.
4. The default step sizes for higher frequency DCT
coefﬁcients are all set to 99.
• Measured items
Average, maximum, and minimum values of enhancement codes per block (16×16 region) in bits
for various combinations of scan = 1, 2, ..., 8, QF2 ＝
40, 45, 50, 55, ..., 90, 95 with step of 5, and three
chrominance formats.
• Measured results
The average enhancement code length per block (16× 16 region) for luminance component of images L1
and H2 is shown in Figure 13. For the 6 images usedin the experiment, the minimum enhancement code
lengths for luminance Y component and chrominance UV components are 4 and 2 bits, respectively.
This means these images have at least one block thatthe entire quantized high frequency DCT coefﬁcientis zero. In case that the quality factor for the higher
frequency DCT coefﬁcients, QF2, is smaller than 50,
the enhancement code lengths per block for chrominance components are mostly 2 bits in both averageand maximum values, therefore it can be concluded
that the higher frequency for chrominance compo-

nents scarcely contribute to the image enhancement.
For comparatively low resolution images L1,
L2, and L3, the enhancement code length per blockfor luminance component is around 100 bits eventhough the quality factor, QF2, is small, when entire
frequency components are used for image enhancement (i.e., scan = 8). The 8×8 DCT coefﬁcients utilized to embed the enhancement codes is at most 64
even if they contain no zero-value DCT coefﬁcients(in general, it is known from studies on watermarking and steganography [6] that an embedding into
zero-value DCT coefficient leads to a large image
degradation), so that, for example, a data embedding
into at least 2 LSBs should be considered for 100-bit
enhancement codes. Because this data embedding
degrades the SNR of lower frequency DCT coefficients, the quality factor for the lower frequency
DCT coefﬁcients, QF1, needs to be sufﬁciently large
in order to keep the visual image quality unchanged
even if such amount of enhancement codes are embedded. Another approach for alleviating this problem is to decrease the number of scans for high frequency DCT coefficients. In this case, however, it
will not be preferable for images that have large highfrequency components, as these components to be
recovered are restricted. How to balance the magnitude of the quality factors, QF1 and QF2, in terms ofoverall visual image quality is a future work.
On the other hand, for the images H1, H2, andH3, which have higher resolution than the images
L1, L2, and L3, the enhancement code length for the
luminance component is less than 100 bits when thequality factor for the high frequency DCT coefficients, QF2, is relatively small, so that the SNR degradation with the LSB embedding is expected to besmall in this case.
4.3 Experiment phase III• Experiment conditions
1. 8 scanning modes are employed (scan = 1, 2, ..., 8).
2. The chrominance formats are 4 : 4 : 4 , 4 : 2 : 2 , and4:1:1.
3. 12 QF1 and 12 QF2 are selected as 40, 45, ..., 90, 95
with step of 5.
4. The default step sizes for higher frequency DCT
coefﬁcients are all set to 99.

― 100 ―

A Novel Approach of Image Enhancement using Steganographic Data Embedding and JPEG Scheme• Measured items
A rate of the amount of enhancement codes againstthat of JPEG bit stream is measured for various combinations of QF1 and QF2.
• Measured results
The rate in percent for images L1 and H2 are shownin Figure 14. As observed in the experiment phaseII, it is obvious that the images L1, L2, and L3 havegreater high frequency components than the images
H1, H2, and H3. Speciﬁcally, the image L2 includes
much high frequency in textures of the cloth. Moreover, the image H1 has very small high frequency
components, while the image H2 has largest highfrequency components among images H1, H2, H3,
because H2 includes thin ropes in the image. It is
concluded that the rate of the amount of enhancement codes against that of JPEG bit stream can be
restricted within 10 % when a combination of higher
QF1 and lower QF2 is used. The enhancement codes
shown here include the codes for two chrominancecomponents, so that the rate slightly decreases by ignoring these codes.
5. CONCLUTION

In this paper, we proposed a new image enhancement method with steganographic data embedding,and measured fundamental property of the method
using various types of images. Although the SNRs
in practice will decrease compared to the results obtained here because this consideration does not include the embedding of enhancement data in the
LSBs of low frequency DCT coefﬁcients, the property of the amount of the enhancement codes against
various quality factor conditions can be obtained.
The experiments performed in section 4 consist
phases I, II, and III, and the following results are obtained: Relatively high quality factor is needed forreconstruct original-size low-passed JPEG compressed image using DCT/IDCT system without entire high frequency components from the experimentphase I. From the experiment phase II, the amountof enhancement codes for luminance component ismuch larger than those for chrominance components.

Moreover, in order to embed the enhancement codes
into the LSBs of low frequency DCT coefﬁcients, it
should be considered that at least 2 LSBs are used
for that embedding. The rate of the amount of enhancement codes against that of JPEG bit stream canbe restricted within about 10 % using an appropriate
combination of quality factors for quantization oflower and higher frequency DCT coefficients fromthe experiment phase III.
The most important problems to implement the
proposed method are how to embed the enhancement
codes into the LSBs of 8×8 DCT coefficients andhow to balance the quality factors of lower and higher frequency DCT coefﬁcients. These issues must be
solved through subjective image quality tests, though
the subjective evaluation depends on many viewing
conditions, such as monitor size, magniﬁcation ratio,
and so on. For this reason, we decided to study the
problem of image quality assessment in conjunction
with the embedding method for the enhancement
data.
Our future topics are to study how to embed theenhancement data and how to balance the quality
factors based on subjective evaluation.

REFERENCES[1] H. Ogawa,“What can we see behind sampling
theorems? ”, IEICE Tech. Report, CAS 2007-124 ,pp.91-96, Mar. 2008.
[2] S. Sugimoto and M. Okutomi,“Super resolution by
image processing,”Journal of the Robotic Society ofJapan, Vol.23, No.3, pp.305-309, Apr. 2005.
[3] A. Katsaggelos, R. Molina, and J. Mateos, Super
Resolution of Images and Video: Chapter 2 Bayesian
formulation of super-resolution image reconstruction,
pp.13-17, Morgan & Claypool, 2007.
[4] http://www.hlevkin.com/TestImages/classic.htm.
[5] Standard High Precision Picture data (SHIPP), IIEEJ,
Apr. 1997.
[6] I. Cox, M. Miller, J. Bloom J. Fridrich, and T. Kalker,― 101 ―

Digital Watermarking and Steganography, 2 nd Ed.,
Morgan Kaufmann, 2008.

Isao FURUKAWA and Junji SUZUKI|H1|, |H14|㻌

L = 8㻌

|H|㻌[dB]㻌

|H|㻌[dB]㻌

|H0|, |H15|㻌
|H2|, |H13|㻌
|H3|, |H12|㻌

L = 9㻌
L = 10㻌
L = 11
L = 12㻌
L = 13㻌
L = 14㻌
L = 15㻌
L = 16㻌

|H4|, |H11|㻌
|H5|, |H10|㻌
|H6|, |H9|㻌
|H7|, |H8|㻌
|H|㻌

fs㻌

fs㻌
Fig. 4 Frequency response ¦Hn¦ and ¦H¦ of DCT/IDCT system withzero high frequency. (N = 16, L = 8)

Fig. 8 Frequency response ¦H¦ of DCT/IDCT system withdiscarding high frequency. (N = 16, L = 8, ..., 16)|H|㻌[dB]㻌

L = 8㻌
L = 9㻌
L = 10㻌
L = 11㻌
L = 12㻌
L = 13㻌(a) L1(b) L2(c) L3

L = 14㻌
L = 15㻌
L = 16㻌(d) H1fs㻌(e) H2
Fig. 10 Test images.

Fig. 5 Frequency response ¦H¦ of DCT/IDCT system with zerohigh frequency. (N = 16, L = 8, ..., 16)

H1㻌

H3㻌

SNRlowpass (dB)㻌

|H|㻌[dB]㻌

(f) H3

H2㻌
L1㻌
L3㻌

L2㻌

|H0|, |H7|㻌
|H1|, |H6|㻌
|H2|, |H5|㻌
|H3|, |H4|㻌
|H|㻌

Quality factor (QF1)㻌
Fig. 11 SNRlowpass vs. quality factor in lower frequency.

fs㻌Fig. 7 Frequency response ¦Hn¦ and ¦H¦ of DCT/IDCT system withdiscarding high frequency. (N = 16, L = 8)

― 102 ―

Enhancement code rate (%)㻌SNRlowres (dB)㻌

A Novel Approach of Image Enhancement using Steganographic Data Embedding and JPEG Scheme

H1㻌

H3
L1㻌
L3㻌
H2㻌
L2㻌

㻌
㻌
㻌
㻌
㻌
㻌

QF2 =
40
45
50
55
60

Quality facor (QF1)㻌

Quality factor (QF1)㻌

(a) image L1

Scan =㻌
8㻌
7㻌
6㻌
5㻌
4㻌
3㻌
2㻌
1㻌

Enhancement code rate (%)㻌Average enhancement code length (bit)Fig. 12 SNRlowres vs. quality factor in lower frequency.

㻌
㻌
㻌
㻌
㻌
㻌

QF2 =
40
45
50
55
60

㻌
㻌
㻌
㻌
㻌

65
70
75
80
85

Quality facor (QF1)㻌

Quality factor (QF2)㻌

(b) image H2(a) image L1

Average enhancement code length (bit)

㻌
㻌
㻌
㻌
㻌

65
70
75
80
85

Fig. 14 Ratio of enhancement codes to JPEG codes.

Scan =
8
7
6
5
4
3
2
1

Quality factor (QF2)(b) image H2
Fig. 13 Average enhancement code length per block.

― 103 ―