Data embedding for vector quantization image processing on the basis of adjoining state-codebook mapping

In this work, a new method is presented for the representation of 3D objects with binary matrix. This method is based on two stages: normalization and quantization. This representation allows us to compare 3D objects by computing the similarity between them. In fact our algorithm compute binary matrix, frequency matrix and cluster coordinates. So we can identify an object by comparing those representations.

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“…Quantization technique is one of the most popular compression techniques [13]. In our case we make the following substitutions:…”

Section: Quantizationmentioning

confidence: 99%

A new representation for 3D objects: Binary matrix

Aznag

Kane

Oirrak

et al. 2015

2015 Third World Conference on Complex Systems (WCCS)

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“…As a result, the traditional RDH methods cannot directly be applied to images in compressed format. Some RDH methods have been proposed so far for various compressed domain, such as vector quantization (VQ) [19], JPEG [20], and block truncation coding (BTC) compressed images [21]- [25], to accommodate different requirements for various applications. Since BTC [26] simply represents an image block by two quantization levels (QLs) and a bitmap, it requires insignificant computational cost but also offers acceptable image quality and compression rate.…”

Section: Introductionmentioning

confidence: 99%

Reversible Data Hiding in Block Truncation Coding Compressed Images Using Quantization Level Swapping and Shifting

2016

KSII TIIS

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The existing reversible data hiding methods for block truncation coding (BTC) compressed images often utilize difference expansion or histogram shifting technique for data embedment. Although these methods effectively embed data into the compressed codes, the embedding operations may swap the numerical order of the higher and lower quantization levels. Since the numerical order of these two quantization levels can be exploited to carry additional data without destroying the quality of decoded image, the existing methods cannot take the advantages of this property to embed data more efficiently. In this paper, we embed data by shifting the higher and lower quantization levels in opposite direction. Because the embedment does not change numerical order of quantization levels, we exploit this property to carry additional data without further reducing the image quality. The proposed method performs no-distortion embedding if the payload is small, and performs reversible data embedding for large payload. The experimental results show that the proposed method offers better embedding performance over prior works in terms of payload and image quality.

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“…Information hiding technique can be realised in spatial domain [1,2] or transformed domain [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. In 2004, Chan and Cheng proposed a data hiding method by simple least significant bit (LSB) substitution with an optimal pixel adjustment process, but it cannot recover the cover image [1].…”

Section: Introductionmentioning

confidence: 99%

New reversible full‐embeddable information hiding method for vector quantisation indices based on locally adaptive complete coding list

Pan

Deng

2015

IET Image Processing

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Steganography based on vector quantisation (VQ)-compressed indices is widely used in information hiding. In this study, the authors propose a new reversible information hiding method for VQ indices using an on-line generated locally adaptive complete coding list. The complete coding list guarantees that all VQ indices can embed one or two secret bits which efficiently increase the embedding capacity. Additionally, they propose a mixed coding method with an index position threshold by exploring the biased-distribution of locally complete coding indices in order to reduce the bit rate. Experimental results demonstrated that the authors proposed method has a better performance in embedding capacity, bit rate and the embedding-efficiency compared with the four information hiding methods.

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Data embedding for vector quantization image processing on the basis of adjoining state-codebook mapping

Cited by 40 publications

References 33 publications

A new representation for 3D objects: Binary matrix

A new representation for 3D objects: Binary matrix

Reversible Data Hiding in Block Truncation Coding Compressed Images Using Quantization Level Swapping and Shifting

New reversible full‐embeddable information hiding method for vector quantisation indices based on locally adaptive complete coding list

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