Data embedding for vector quantization image processing on the basis of adjoining state-codebook mapping
Introduction
The Internet has become a convenient and common information source in people’s daily life. Digital multimedia, one of the most popular data representations on the Internet, is attracting more and more people to use it for different purposes [9], [14], [27], [30], such as entertainment and information sharing. For example, people can watch music videos from a video-sharing websites; they can also share all kinds of pictures with their friends on social networking websites. Along with these trends, a key-concern that has emerged is multimedia copyright which has become an important issue and branch in both studies and practical applications closely associated with the digital information society. Secret embedding in digital media is one way to cope with the need for the media-owner to declare their ownership by embedding a secret or key-information into the cover-media, but still keep the same quality for the stego-media. Information hiding for images is one of the research issues in digital multimedia. It provides a way to certify copyrights of image files, as well as a means of secret communication for people. There are many popular image hiding approaches in the literature [1], [2], [3], [4], [5], [6], [7], [8], [10], [12], [13], [15], [17], [18], [19], [21], [22], [23], [24], [25], [26], [28], [29], [31], [32], [33], [34], such as spatial-domain-based schemes [1], [3], [10], [12], [13], [23], [32], frequency-domain-based schemes [2], [4], [8], [19], [24], [26], and compression-domain-based schemes [5], [6], [7], [17], [18], [21], [29], [31], [33], [34]. The spatial-domain-based schemes utilize spatial information such as differences of pixel values [32], histogram [11], [12], difference expansion [1], [13], and least significant bits [3], [23]. The frequency-based schemes utilize frequency-based techniques such as discrete cosine transform [2], [24], [26], discrete wavelet transform [4], [19], and discrete Fourier transform [8]. The compression-based schemes [6], [7], [17], [18], [21], [29], [31], [33], [34] utilize popular image-compression algorithms such as Vector quantization (VQ) [20]. They can embed more secret data (e.g. copyright information) into the cover image than other types of schemes, and yet achieve higher compression rates.
The VQ-based hiding schemes may produce different output formats – those without extra control messages [6], [29], [33], and those with control messages [21], [31], [34]. The first types of schemes produce VQ or side-match VQ (SMVQ) images that do not include any extra control messages. The second types of schemes use various coding/decoding rules to embed secret data into a cover image file, and produce a code-stream with extra control messages. They usually have better compression rates for the output code-stream and higher embedding capacity for secret data when compared with the first types of schemes. A VQ-based hiding scheme may support reversibility, which refers to the ability to retrieve the original cover image from the stego-image or the compressed code-stream. Reversible information hiding schemes [5], [6], [21], [31], [34] have become very popular in recent years since they are able to retrieve the original cover images as well as the embedded secret data. However, a reversible scheme has more restrictions on the output format, and thus may have a negative impact on the amount of secret data and the quality of the stego-image. On the other hand, an irreversible information hiding scheme [17] need not support reversibility, and thus can embed more secret data into the cover image or achieve a better quality of stego-image.
In this paper, we propose a new VQ-based scheme for reversible data hiding. It produces a compressed code-stream along with control messages, and achieves a high compression rate and large embedding capacity. We improve the SMVQ-based scheme by using a technique, called Adjoining State-Codebook Mapping (ASCM), to embed secret data. The ASCM uses the information of the neighboring blocks to create two state-codebooks. The state-codebooks are then used for encoding and decoding. Experimental results showed that our method outperforms recent similar work, Chang et al.’s scheme and Yang et al.’s scheme.
The rest of this paper is organized as follows. Section 2 introduces vector quantization, side-match VQ and related embedding schemes. Section 3 presents the proposed scheme. Section 4 shows the experimental results, and compares them with Chang et al.’s method and Yang et al.’s method. Finally, Section 5 concludes this paper.
Section snippets
Background
In this section, we will introduce the background knowledge of VQ [20] and SMVQ [16]. Then we will briefly introduce recent related work: Chang et al.’s scheme [5] and Yang et al.’s scheme [34].
The proposed scheme
This section describes the details of the proposed scheme, which uses the state-codebook concept of SMVQ and a novel technique, called Adjoining State-Codebook Mapping (ASCM), to achieve high compression rate and large embedding capacity. The concept of ASCM is described in Section 3.1. The details of the encoding and decoding procedures are presented in Sections 3.2 Data embedding, 3.3 Data decoding respectively.
Experimental results and discussions
We have conducted several experiments to evaluate the performance of the proposed ASCM scheme, and compared the proposed scheme with Chang et al.’s scheme [5] and Yang et al.’s scheme [34]. There are nine images shown in Fig. 9 used for the experiments with a computer Intel Core i5-3550 CPU 3.30 GHz and the software Dev C++. In our experiments, all the nine images are encoded by VQ, and the codebook consists of 512 codewords. Their PSNR values are 32.248 dB for image Lena, 31.408 dB for image
Conclusions
In this paper, we proposed a new lossless data-hiding scheme in the VQ domain. By adopting the concept of Adjoining State-Codebook Mapping (ASCM), it achieves a high embedding capacity and compression bit rate. The ASCM technique first creates two state-codebooks, which are obtained from the left adjacent block and the upper adjacent block of the encoding block, and then maps the content of the current encoding block to an index in the state-codebooks to reduce the size of the output
Acknowledgments
This research was partially supported by the National Science Council of the Republic of China under the Grant NSC 100-2221-E-015-001-MY2-, NSC 101-2218-E-008-003-, and the Software Research Center, National Central University, Taiwan.
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