適応量子化ウェーブレット変換電子透かしの絵画画像への適用
前村 葉 子A watermarking scheme for images of painting arts based on wavelet
transform using adaptive quantization
Yoko Maemura

Abstract
The digital image watermarking is studied for both copyright protection and content
authentication and needed to be better able to survive image processing operations andto be less degradation of picture quality. The characteristics of the watermark scheme
is depends on the property of contents. This work deals with the image watermarkingof the oil painting arts. The watermarking schemes based on the wavelet transformhave many utilization for the digital image contents because it is able to consider thehuman vision system. This paper reports findings in the experiments of the image
watermarking scheme based on wavelet transform for the oil painting arts.

Keywords
Watermarking, Painting art, Digital picture, Picture quality, Discrete wavelet
transform

1. Introduction
The explosive growth of digital multimedia techniques and digital network
communication has created a processing demand for techniques used for copyrightprotection and content authentication. Digital watermarking has been proposed as away to meet these requirements [1]. A digital watermark encodes the owner’s license
information and embeds it into data. The many requirements for an acceptable
technique of watermarking can be seen in ref [2]. Conventional watermarking schemes
can be classified into two types, the space domain and the frequency domain. The
robustness and the imperceptibility represent the most important characteristics. The
several previous researches indicate that they can be more easily obtained in thefrequency domain. Discrete wavelet transform (DWT) is popular in many image
applications due to the unique feature of multi-resolution representation [3]. In addition,
human vision system (HVS) models have been widely used in digital watermarking tominimize the visual effects of the watermark while increasing the strength ofwatermark [4][5]. Original image is transformed by taking two or three-level DWT. The

watermarks are embedded into the image of wavelet coefficients from the second or thethird levels. Especially, the quantization steps depend on the property or function ofwatermarks, and they are adaptive to the image texture feature. This is the mostimportant points in this paper. The utilization of characteristics of human vision leads
to control watermark strength adaptively according to the picture contents. The
painting arts have many different properties [6] from the natural still images from theviewpoints of HVS and these characteristics clarified by using the wavelet transformwatermark schemes are described in the following. Simulation results demonstrate theeffectiveness of the watermarking method for the painting arts in terms of quality of theembedded image.

2. The watermark scheme
2.1 The watermark in wavelet domain
The DWT of digital image data has been focused because it was adopted on theimage compression technique, JPEG2000 and MPEG4. Since the watermarking isdesigned for invisible watermarks, invisibility of the watermark in a watermarked
image should be take special consideration. HVS theory tells that human eyes are
sensitive to the changes in low frequency part of image. Therefore, it is desirable toselect the low-high(LH), high-low(HL), high-high(HH) wavelet coefficients forembedding sub-band for the HVS. However, the robustness is decreasing in highfrequency components because the high frequency components can be removed
thorough the typical image processing such as JPEG compression and noise reduction
etc. Figure 1 shows the sub-band structure using two decomposition level.

2.2 The embedding and detection process
The main steps performed in the present watermarking system are summarized infigure 2. Compute the wavelet transform of the host signal (original image data) to get
the sub-band coefficients. The original image is decomposed into 7 sub-bands by taking
two-level DWT. The quantization step is selected by taking the small range block
analysis of the original image data. Those are just classified into two type block;flat/non-flat in the view from HVS. This classification is calculated from the standard
deviation for each range block. The small range block size of original image is 4x4pixels. The robustness depends on the magnitude of quantization step. The
quantization step depends on the complexity of the small range block evaluated by thestandard deviation. The quantization step for the flat block and non flat block is set to

M1 and M2, (M1 < M2) respectively. The robustness suppressed by the reduced
embedding strength M1 is compensated by using the error correct method as amajority-logic. The watermark signal is embedded cyclic every 8x8 block in low-low
2-level (LL2) component of decomposed image. This justly leads to decreasing theamount of embedding bits. The results of decision which block is flat or non-flat arerecorded for the detection process.
The detecting process also starts from the DWT decomposition and thequantization step used for the embedding process is detected by the block analysis of
the watermarked image as same as embedding process.

original image

LL2 HL2
HL1
LH2 HH2DWT

Block analysis

LH1

Adaptivequantization
modulation

Quantization
step selection

HH1

Figure 1. Sub-band decomposition

IDWTstructure.

watermarked
image
Figure2.

Blockdiagramofwatermark

embedding process.

3. The performance analysis
To evaluate the performance, the three kinds of still image are used for thesimulation. One is the “Lenna” which is shows in figure 3(a) as known SIDBA standard
still image. The other two are the artificial oil painting images of famous self-portraits(figure 3 (b) and (c) are by Picasso and Matisse, respectively. Those painting images
have three steps processing for this experiment: resizing for reasonable size following
the copyright low in 2009 [7], cropping into same size of Lenna (256x256) and the color
changing from full-color to 8bit gray scale. The Lenna includes both the textured areawith high frequency components and the large homogeneous areas. On the other hand,the painting images have opposite properties.
In the watermark extraction, the objective measurement, peak signal-to-noise(PSNR) is employed. The results of PSNR between the original image and thewatermarked image are shown in figure 4. The parameters are set as same asreference[8]; threshold T=7, M2=7, M1=2～7, Level=2( figure 4(a)), Level=3(figure 4(b)).
For all sample images, the same tendency to M1 which is mentioned in the previous
work [5] can be reconfirmed in this result.

The robustness for dewatermark especially for JPEG compression have good
property in the previous evaluation[5]; more than 80% detection rate for M1 =4～7 inthe condition of the JPEG quality factor more than 30% . Hereafter, the picture quality
had been evaluated in the condition of M1=4 and 7 for level2 and M1=4 for level3. The
evaluation method used in this experiment was the standard subjective method
described in Recommendation ITU-R BT.500-7 [8] and also described in the guideline
published by Institution of Television Engineers of Japan [9]. Each watermarked
picture was shown, after the original picture, to eleven evaluators who rated thequality of the watermarked pictures according to the following scoring rules: score 5
when the watermarks are “imperceptible”, 4 when they are “perceptible but notannoying”, 3 when they are “slightly annoying”, 2 when they are “annoying”. The
average of the eleven scores was used as the quality level. Table 1 compares theevaluation results for three sample pictures. From figure 4 and table 1, the following
results can be seen:
The PSNR of Lenna was almost the same as the other two samples when thewatermark strength M1 was 7, but the quality of Lenna was degraded. When
the watermark strength M1 was 4, the good quality of Lenna was obtainedwith the PSNR compared with the Matisse. On the other hand, the quality
score of Lenna was degraded compared with other two painting samples. The
reason seems to be that watermarks are more perceptible in pictures havingsmooth areas like Lenna than in those having messy areas like the otherpainting pictures.

The good quality score were obtained for all pictures when the level was set at
3 but the PSNR was reduced for all ranges of the strength M1 compared withthe case of level 2. For example, when the watermark strength M1 was 4, thequality score of Picasso with watermarked with level 3 was 0.09 point higher
than that of Picasso watermarked with level 2. This property also can be seenin Lenna but not so slightly, because the messiness increased when the block
size was increased. This is due to the results of entropy analysis of the painting
art [6].

4. Conclusions
The watermarking schemes depend on the characteristics of the picture contents
were studied and especially for the oil painting pictures were treated in the present
work. The watermarks are more perceptible in pictures having smooth planes like
Lenna than in those having messy planes like the painting pictures. Especially for oilpainting, a lot of the messy plane due to the painting touch and the color variation canbe created in the whole of picture. From these reasons, the wavelet transform based
watermark was adopted because it can deal with the components of the frequencies
and the multi-resolution representation (the registrations of the arts contents needhigh resolution representation). The performances were analyzed with the PSNR andthe quality evaluation. In this scheme, the quantization step for the wavelet
coefficients means the strength of the watermark. These watermarks are adaptively
embedded into different strength depending on the messiness evaluated by thestandard deviation of the treated part of the image. This leads to the higher quality ofthe painting picture rather than the natural still picture. Future work will therefore
focus on the analysis from the viewpoints of the correlations between the number ofblocks per one flame of painting picture and the entropy of the pixel variations per one
flame.
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(a)Lenna(b)Picasso(c)Matisse

Figure 3 sample pictures(a) Level=2(b) Level=3

Figure 4 PSNR of sample pictures.
Table 1 Quality scores
Level=2

Level=3

Image

M1=7

M1=4

M1=4

Lenna

3.00

3.45

3.55

Picasso

3.55

4.45

4.64

Matisse

3.97

4.36

4.45