A REVERSIBLE DATA HIDING SCHEME TO INVERSE

HALFTONING

Jia-Hong Lee, Hong-Jie Wu

Department of Information Management, National Kaohsiung First University of Science and Technology

Kaohsiung, Taiwan, R.O.C

Mei-Yi Wu

Department of Information Management, Chang Jung University, Tainan, Taiwan, R.O.C.

Keywords: Halftone image, Inverse halftoning, Reversible data hiding, Look-up table (LUT), Gaussian filtering.

Abstract: A new inverse halftoning algorithm based on reversible data hiding techniques for halfton images is

proposed in this research. The proposed scheme has the advantages of two commonly used methods, the

look-up table (LUT) and Gaussian filtering methods. We embed a part of important LUT templates into a

halfton image and restore the image lossless after these templates been extracted. Then a hybrid method is

performed to reconstruct a gray-scale image from the halfton image. In the image reconstruction process,

the halfton image is scanned pixel by pixel. If the scanned pattern surrounding a pixel appeared in the LUT

templates, a gray value is directly predicted using the LUT value, otherwise, it is predicted using Gaussian

filtering. Experimental results show that the reconstructed gray-scale images using the proposed scheme

own better quality than both the LUT and Gaussian filtering methods.

1 INTRODUCTION

Inverse halftoning is a process which transforms

halftone images into gray-scale images. Halftone

images are binary images that provide a rendition of

gray-scale images and consists of ‘0” and ‘1”. It has

been widely used in the publishing applications,

such as newspapers, e-documents, magazines, etc. In

halftoning process, it need to use a kernel to carry

out the conversion, and the common kernel is such

as Floyd-Steinberg kernel, and is difficult to recover

a continuous-tone image through halftone

manipulation, conversion, compression, etc. In the

past few years, many efficient inverse halftoning

algorithms have been proposed, but there is no way

to construct a perfect gray image from the given

halftone image. There exist several inverse

halftoning methods, including kernel estimation

(Wong, 1995), wavelet (Xiong, Orchard, &

Ramchandran, 1996), filtering (Fan, 1992; Kite,

Venkata, Evans, & Bovik, 2000), and set theoretic

approaches (Chang, Yu, & Lee, 2001). Most of these

methods can obtain good reconstruction image

quality but require relatively high computational

complexity.

The halftoning and inverse halftoning processes can

be regarded as the encoding and decoding processes

of vector quantization. Therefore, the codebook

design methods can be applied to build the inverse

halftoning lookup tables (Mese & Vaidyanathan,

2001, Chung & Wu, 2005). The content of a table

entry is the centroid of the input samples that are

mapped to this entry. The results are optimal in the

sense of minimizing the MSE for a given halftone

method. Although the table lookup method has the

advantages of good reconstructed quality and fast

speed, it faces the empty cell problem in which no or

very few training samples are mapped to a specific

halftone pattern.

In this paper, a reversible data hiding scheme for

halftone images is proposed. We embed a part of

important LUT templates into a halfton image and

restore the image lossless after these templates been

extracted. Then a hybrid method is performed to

reconstruct a gray-scale image from the halfton

image.

Lee J., Wu H. and Wu M. (2009).

A REVERSIBLE DATA HIDING SCHEME TO INVERSE HALFTONING.

In Proceedings of the International Conference on Signal Processing and Multimedia Applications, pages 86-89

DOI: 10.5220/0002237500860089

 SciTePress

2 REVERSIBLE DATA HIDING

FOR BINARY IMAGES

Reversible data hiding can embed secret message in

a reversible way. Relatively large amounts of secret

data are embedded into a cover image so that the

decoder can extract the hidden secret data and

restore the original cover image without any

distortion. Recent, a boundary-based PWLC method

is presented (Tsai, Chiang, Fan, Chung, 2005). This

method defines the same continuous 6 edge pixels as

an embeddable block through searching for binary

image edges. And then one can embed data in the

pair of the third and fourth edge pixels. A reversible

data hiding method for error diffused halftone

images is proposed (Pan, Luo & Lu, 2007). This

method employs statistics feature of pixel block

patterns to embed data, and utilizes the HVS

characteristics to reduce the introduced visual

distortion. The method is suitable for the

applications where the content accuracy of the

original halftone image must be guaranteed, and it is

easily extended to the field of halftone image

authentication. However, these two methods both

have a drawback that the capacity of data hiding is

still limit.

3 PROPOSED METHOD

The proposed inverse halftoning method based on

reversible data hiding techniques can be divided into

two phases: the embedding process and the

extracting process. Figure 1 shows the diagram of

the proposed method. In the embedding process, a

gray-scale image is transferred into a halftone image

by error diffusion process. Then pattern selection is

performed to determine the pattern pairs for the use

in reversible data hiding. Meanwhile, a part of LUT

templates are selected to keep high quality of

recovery images in the reconstruction process. These

templates along with the pattern pairs will be

encoded in bit streams and embedded into the

halftone image. The data embedding operation is

performed based on pattern substitution. In the data

extracting process, the pattern pairs and LUT

temples are first extracted. The halftone image can

be lossless restored after the data extraction. Finally,

we can reconstruct a good quality gray-scale image

from the halftone one with the aid of LUT templates.

The proposed scheme has the advantages of two

commonly used methods, the look-up table (LUT)

and Gaussian filtering methods. We embed a part of

Figure 1: The embedding and extracting diagram of

proposed method.

important LUT templates into a halfton image and

restore the image lossless after these templates been

extracted.

3.1 Data Hiding with Pattern

Substitution for Halftone Images

The proposed method of reversible halftone data

hiding technique uses pattern substitution method to

embed and extract data into halftone images. The

original image is partitioned into a set of non-

overlapping 3×3 blocks. There are totally 2

different

patterns. Therefore, each pattern is uniquely

associated with an integer in the range of 0 to 511.

In most cases, many patterns never appear in an

image. Figure 2 is an instance to show the pattern

histogram for image Lena.

Figure 2: The pattern histogram of halftone image “Lena”.

In this study, all patterns are classified into two

groups,

used

and

unused

. For each used pattern A,

an unused pattern B which content is the most

closest to pattern A will be selected to form a pair

for data embedding. In the data embedding process,

the original halfton image is partitioned into a group

of 3×3 non-overlapping patterns. Then, any pattern p

A REVERSIBLE DATA HIDING SCHEME TO INVERSE HALFTONING

on the halfton image with the same content of A will

be selected to embed 1-bit data. If a data bit “0” is

embedded on p, then the content of p is remained as

A. If a data bit “1” is embedded on p, then the

content of p is updated as the content of pattern B.

This scheme works because pattern A, B look

similar. In data extraction process, the embedded

message is obtained depending on the pattern A, B

when the test image is scanned. To achieve a higher

capacity of embedding data, more pattern pairs

should be determined, whose steps can be presented

as below.

1. Partition the original image into non-overlapping

3 ×

3 blocks.

2. Compute the occurrence frequencies for all

appeared patterns. Sort these used patterns

decreasingly and denoted them as

according

to their occurrence frequencies.

3. Find out all unused patterns. Assume that there

are totally k unused patterns, k pairs of patterns

(

) should be constructed to perform the

data embedding.

4. Search all blocks in the original image. As long

as we come across a pattern in the

, if a bit

“0” is embedded, the block is remained as

;

Otherwise the block is updated as the pattern

Figure 3 displays the top 10 patterns

and 10 unused

patterns

from Lena image.

Figure 3: An example of

(first row) and

row)

obtained from the Lena image.

However, the image quality of stego-image

generated using the proposed method is not very

well in the visual effect. Figure 4(a) shows the

stego-image obtained using the proposed method

with 26317 bits embedded into Lena.

To consider human visual effect, we should take

notice about some situations which will cause

“Congregation” effect of bright or dark spots. These

cases are displayed bellows. To avoid these cases

when a pattern replacement occurs, we adjust the

weights of distance on pattern similarity

computation and a better result is obtained. Figure

4(b) shows a good image quality for the stego-image.

(a) (b)

Figure 4: The stego-image generated using the proposed

method; (a) without quality consideration (b) with quality

consideration.

Figure 5: The cases will cause bad human visual effects.

3.2 Determine Important LUT

Templates to be Embedded

The proposed method is a kind of hybrid inverse

halftoning method which has the advantages of

Gaussian filtering and LUT methods. Figure 6

shows the reconstruction process for these two

methods. For an image block in the halfton image,

if the difference of predicted value and the original

real gray value using Gaussian filtering method is

larger than the difference using LUT method, then

the LUT template is worth to be recorded and

embedded. This means the LUT template can obtain

a higher image quality than using Gaussian filtering

method in the image recovery process. However,

only a part of important templates which save larger

quality loss are selected to embed since the

embedding capacity is limit for a halftone image. In

Figure 6: An instance to determine the “importance” for a

LUT template.

SIGMAP 2009 - International Conference on Signal Processing and Multimedia Applications

the gray-scale image recovery process, we scan the

halftone image by checking the templates. If the

current template is one of the embedded templates,

then LUT is used to predict the gray value;

otherwise Gaussian filtering method is applied to

predict the value.

4 EXPERIMENTAL RESULTS

Four 512×512 error diffused halftone images “Lena”

、“pepper”、“Airplane”、“Baboon” are selected to

test the performance of the proposed method. These

halftones are obtained by performing Floyd–

Steinberg error diffusion filtering on the 8-bit gray

level images. Capacities for different images are

listed in Table 1. Table 2 shows the PSNR values for

the recovery images using the different methods.

The proposed method performs better than both

Gaussian filtering and LUT method (train 10

images). Experimental results show that the

reconstructed gray-scale images using the proposed

scheme own better quality than both the LUT and

Gaussian filtering methods.

Table 1: The embedding capacity (bits) with different

images using the proposed method.

Table 2: PSNR values for the reconstructed images using

different methods.

5 CONCLUSIONS

A new inverse halftoning algorithm based on

reversible data hiding techniques for halfton images

is proposed in this research. We embed a part of

important LUT templates into a halfton image and

restore the image lossless after these templates been

extracted. Then a hybrid method is performed to

reconstruct a gray-scale image from the halfton

image. Experimental results show the proposed

scheme outperformed than both the LUT and

Gaussian filtering methods.

ACKNOWLEDGEMENTS

This work was supported by National Science

Council, R.O.C., under grant 97-2221-E-390-012.

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A REVERSIBLE DATA HIDING SCHEME TO INVERSE HALFTONING