Side-match Vector Quantization Research Articles

Adaptive Lossless Data Hiding Scheme for SMVQ-Compressed Images using SOC Coding

Lossless data hiding recovers original images precisely after secret data are extracted. Therefore, it has received considerable attention among researchers. This paper offers an adaptive lossless data hiding scheme that is based on the search-order coding (SOC) algorithm for side match vector quantization (SMVQ) compressed images. By combining SOC coding with the SMVQ algorithm, compression performance is further improved while a large amount of free space is generated to hide secret data during the data embedding phase. In the extracting phase, simple steps are used to extract that embedded secret data. The same index table can be reconstructed on the receiver side, which ensures that this proposed scheme can restore the original cover image exactly. Experimental results have demonstrated that the proposed scheme yields a higher embedding rate and a lower compression rate than other related VQ and SMVQ based data hiding schemes.

A Survey on Joint Data-Hiding and Compression Techniques based on SMVQ and Improved Locally Adaptive Coding Method

Today Reversible data hiding is latest research area in the field of secret data hiding technique. Data hiding is a technique in which conceal secret data is hidden in some cover image like video, audio, movie, file, and image. Data hiding popularly used in medical and military application. This paper covers many hiding and compression techniques like vector quantization (VQ), side match vector quantization (SMVQ), histogram shifting technique, locally adaptive data compression technique but main focus in this paper is on side match vector quantization.

Reversible joint fingerprinting and decryption based on side match vector quantization

This paper proposes a joint fingerprinting and decryption (JFD) based on side match vector quantization (SMVQ) for transmitting confidential information and traitor tracing in multimedia distribution. With the help of SMVQ, the proposed scheme can extract the fingerprint without referring to the original image. In addition, after the extraction of the fingerprint, the fingerprinted copy can be completely recovered to the un-fingerprinted version. The experimental results show that the proposed scheme can achieve high perceptual security (with PSNR below 12 on average), high fingerprinted visual quality (with PSNR above 28 on average), and desirable fingerprint payload (about 0.6bit/per block).

Low bit-rate information hiding method based on search-order-coding technique

Information hiding method with low bit rate is important in secure communications. To reduce bit rate we propose a new embedding method in this paper based on SOC (search-order coding) compression technique. Compared to Chang et al.’s scheme in 2004, our scheme completely avoids the transform from SOC coding to OIV (original index values) coding to significantly reduce bit rate. In order to further reduce bit rate, Chang et al. proposed a reversible data hiding scheme using hybrid encoding strategies by introducing the side-match vector quantization (SMVQ) in 2013. But it needed additional 1 bit indicator to distinguish the two statuses to determine OIV is belonged to G1 or G2. This overhead gave a large burden to compression rate and could not reduce the bit rate significantly. In contrast, our scheme completely avoids this indicator. The experimental results show that the proposed method can efficiently reduce the bit rate and have the same embedding capacity compared with Chang et al.’s scheme in 2004 and Chang et al.’s scheme in 2013. Moreover, our proposed scheme can also achieve a better performance in both the embedding capacity and bit rate than other related VQ-based information hiding schemes.

An efficient reversible data hiding scheme based on SMVQ

Data hiding technique can hide a certain amount of secret data into digital content such as image, document, audio or video. Reversible compressed image data hiding can loosely restore the cover image after extracting the secret data from the stego-image. In this paper, we present an efficient reversible image data hiding scheme based on side match vector quantisation. Mapping concept is useful for this scheme because it converts the ternary into binary. The proposed scheme significantly increases the payload size of a block, and the quality analysis of the proposed scheme showed that it contains a better peak signal to noise than other schemes.

A Novel Joint Data-Hiding and Compression Scheme Based on SMVQ and Image Inpainting

In this paper, we propose a novel joint data-hiding and compression scheme for digital images using side match vector quantization (SMVQ) and image inpainting. The two functions of data hiding and image compression can be integrated into one single module seamlessly. On the sender side, except for the blocks in the leftmost and topmost of the image, each of the other residual blocks in raster-scanning order can be embedded with secret data and compressed simultaneously by SMVQ or image inpainting adaptively according to the current embedding bit. Vector quantization is also utilized for some complex blocks to control the visual distortion and error diffusion caused by the progressive compression. After segmenting the image compressed codes into a series of sections by the indicator bits, the receiver can achieve the extraction of secret bits and image decompression successfully according to the index values in the segmented sections. Experimental results demonstrate the effectiveness of the proposed scheme.

A scheme of reversible information hiding based on SMVQ

In this paper, we propose two reversible information hiding schemes based on side-match vector quantisation (SMVQ). In 2006, Chang et al. proposed a data-hiding scheme based on SMVQ coding. In order to increase both the image quality and the embedding capacity, we improve their method by embedding multiple secret bits into a block and finding out the second selected codeword from the full codebook. In addition, we propose another reversible information hiding scheme whose output is a pure VQ index table. The weighted bipartite graph is used to model our scheme and the matching approach is used to find out the best solution. Compared with Chang et al.’s scheme, we have higher visual quality in the experimental results.

A novel VQ-based reversible data hiding scheme by using hybrid encoding strategies

Reversible data hiding is a special algorithm that not only guarantees the confidential data will be extracted accurately but also allows the original cover image to be reconstructed without distortion after the confidential data are completely extracted. This paper proposes a new index compression and reversible data hiding scheme based on side-match vector quantization (SMVQ) and search order coding (SOC). In this proposed scheme, the confidential data are embedded into the transformed index table of a cover image. During the extracting phase, simple steps are employed to extract the confidential data and reconstruct the original cover image. The experimental results show that with a codebook size of 256, the average compression rate of the proposed scheme is 0.325bpp, which is superior to that of the methods proposed by Chen and Huang (0.426bpp) and Chang et al. (0.429bpp). Additionally, the embedding and extracting times of our scheme are 5.491s and 0.352s, respectively, demonstrating that the execution time of the proposed scheme is much faster than that of the methods of Chen and Huang and Chang et al. Moreover, our scheme achieves better performance than other selected reversible data hiding schemes with respect to the data embedding and data compression rates.

Reversible and high-payload image steganographic scheme based on side-match vector quantization

A reversible and high-payload image steganographic scheme implemented in the SMVQ compression domain of image is proposed. This scheme focuses both on the payload of the compressed cover image at the transmitter and on the visual quality of the restored cover image at the receiver. In addition, keeping an acceptable bit rate for the cover image is another goal of the proposed scheme since the secret data are sheltered by and delivered through the compression codes. Moreover, the proposed scheme is reversible. The original SMVQ-compressed cover image can be losslessly restored from the compression codes at the receiver. Simulation results show that the proposed scheme is feasible for secret communication.

A capacity‐enhanced reversible data hiding scheme based on SMVQ

In recent years, there are many reversible data hiding schemes proposed. Most schemes are guaranteed that the original cover image can be reconstructed completely. When the secret data are hidden in the compression image, the receiver need to extract the secret data, reconstruct the original cover image and compress the image to save the space. In 2006, Chang et al. proposed a reversible data hiding scheme based on side match vector quantisation (SMVQ). Their method can extract the secret data and reconstruct the SMVQ‐compressed cover image. In this paper, we proposed a capacity‐enhanced reversible data hiding scheme. The hiding payload size of secret data of our proposed scheme is more than those of both Chang and Wu’s and Chang et al.’s schemes for VQ‐ and SMVQ‐based compressed images.

Steganography scheme based on side match vector quantization

We propose a steganographic method that is based on side match vector quantization SMVQ. The secret data are encrypted and then embedded in an SMVQ-decompressed image. In the decoding pro- cess, both the hidden secret and the original SMVQ code can be recon- structed without any error. Experimental results demonstrate that the proposed method provides a high hiding capacity and acceptable stego- image quality, to an extent comparable to a well-known SMVQ-based reversible steganographic method. © 2010 Society of Photo-Optical Instrumenta-

High capacity SMVQ-based hiding scheme using adaptive index

This paper proposes a high-capacity image-hiding scheme based on an adaptive index. Data-hiding based on vector quantization (VQ) is a technique for hiding data in the VQ index code. Data-hiding based on side match vector quantization (SMVQ) has been proposed for improving the compression rate of VQ-based data-hiding schemes. However, the hiding capacity of an SMVQ-based data-hiding scheme is very low since, at most, only one secret bit is hidden in one index code. To overcome this drawback and increase the capacity, the proposed method uses an adaptive index to hide more bits in one index code. The weighted squared Euclidean distance (WSED) can also be used to increase the probability of SMVQ to get greater hiding capacity. According to the experimental results, a higher hiding capacity was obtained and a good-quality embedded image was preserved in the adaptive index SMVQ-based data-hiding scheme. The hiding capacity of the proposed scheme was approximately twice that of relevant two hiding schemes.

무질저한 SMVQ 기반의 제로-워터마킹

디지털 워터마킹은 디지털 콘텐츠에 워터마크를 삽입함으로써 불법적인 복제를 방지하고, 지적재산권 및 저작권을 보호하며, 소유권을 주장할 수 있는 근거를 제시하는 기술이다. 일반적으로 워터마킹 기법에서는 워터마크를 삽입함으로써 데이터 왜곡과 품질 저하가 불가피하다는 단점이 있다. 이를 극복하기 위하여 원래 데이터를 변경하지 않는 제로-워터마킹 기법들이 최근 제시되고 있다. 이 논문에서는 VQ(Vector Quantization) 방식의 블록 효과를 줄이고, 압축 비율과 품질을 향상시킨 SMVQ(Side Match Vector Quantization) 방식에 대한 제로-워터마킹 체계인 CSMVQ(Chaotic SMVQ)를 제안한다. SMVQ 이미지 압축에서는 동일하게 두 이웃 블록의 접면 정보를 이용하기 위하여 좌측 상단에서 우측 하단으로 진행되므로, 임의의 순서로 블록을 선택하여 워터마크를 삽입할 수 없다. CSMVQ에서는 이전 에 부호화된 (1개에서 4개까지의) 이웃 블록들의 접면 정보를 동적으로 고려하여 부호화를 진행하므로, 무질서한 방식으로 워터마크가 삽입되도록 지원할 수 있다. 이 기법을 적용한 이미지의 품질이 SMVQ보다 우수하며, 샤프닝, 메디안 필터링 등을 이용한 공격에도 워터마크가 강인함을 보여준다. Digital watermarking is a technology for preventing illegal copying, for protecting intellectual property rights and copyrights, and for suggesting grounds of the ownership by inserting watermarks into digital contents. Generally speaking, watermarking techniques cannot escape from data distortion and quality degradation due to the watermark insertion. In order to overcome the shortcoming, zero-watermarking techniques which do not change the original data have been proposed recently. This paper proposes CSMVQ(Chaotic SMVQ), a zero-watermarking system for SMVQ(Side Match Vector Quantization) which shows better compression ratio and quality and less blocking effect than VQ(Vector Quantization). In SMVQ, compression progresses from left top to right bottom in order to use the information of the two neighbor blocks, so it is impossible to insert watermarks chaotically. In the process of encoding, CSMVQ dynamically considers the information of the (1 to 4) neighbor blocks already encoded. Therefore, watermark can be inserted into digital contents in chaotic way. Experimental results show that the image quality compressed by CSMVQ is better than that of SMVQ and the inserted watermark is robust against some common attacks.

A path optional lossless data hiding scheme based on VQ joint neighboring coding

Data hiding is an important technique for covert communication that embeds secret data into a cover image with minimal perceptible degradation. Lossless data hiding is a special type of data hiding technique that guarantees not only the secret data but the cover media can be reconstructed without any distortion. In this paper, a novel path optional lossless data hiding scheme based on the joint neighboring coding (JNC) of the vector quantization (VQ) index table is proposed. The proposed scheme generates a VQ index table based on the cover image first. Next, according to an initial key and secret data content, different adjacent indices may be chosen to perform joint neighboring coding for each index and hide secret data. Finally, an appropriate output codestream is generated based on the minimal length principle. Our main contributions lie in three aspects: (1) the method combines the novel path_based shift method with the traditional JNC method to improve the capacity and stego image quality simultaneously. (2) The secret data extraction and cover image recovering processes are separated and both can be publicized to users. (3) The method is path optional to meet various users’ requirements. To testify the superiority of the proposed method, we compare it with the side match VQ (SMVQ)-based and modified fast correlation VQ (MFCVQ)-based algorithms. According to the experimental results, the proposed scheme outperforms the SMVQ-based and MFCVQ-based algorithms in four aspects, i.e., hiding capacity, stego image quality, transmission efficiency and security level.

An improved lossless data hiding scheme based on image VQ-index residual value coding

Copyright protection and information security have become serious problems due to the ever growing amount of digital data over the Internet. Reversible data hiding is a special type of data hiding technique that guarantees not only the secret data but also the cover media can be reconstructed without any distortion. Traditional schemes are based on spatial, discrete cosine transformation (DCT) and discrete wavelet transformation (DWT) domains. Recently, some vector quantization (VQ) based reversible data hiding schemes have been proposed. This paper proposes an improved reversible data hiding scheme based on VQ-index residual value coding. Experimental results show that our scheme outperforms two recently proposed schemes, namely side-match vector quantization (SMVQ)-based data hiding and modified fast correlation vector quantization (MFCVQ)-based data hiding.

Image steganographic scheme using predictive side match vector quantisation

Information hiding or steganography involves embedding significant data into various forms of digital media such as text, audio, image and video secretly. With the rapid development of network technology, information hiding techniques have been widely applied in copyright protection, fingerprinting and secret communication. In this paper, an image steganographic scheme capable of hiding considerable quantities of secret data while preserving acceptable visual quality for cover images is proposed. The proposed scheme is applied on the compressed domain of image. The major idea of this scheme is to hide secret data into the compressed codes of cover image during the encoding process of side match vector quantisation (SMVQ) such that the interceptors will not capture the secret data. The proposed steganographic scheme takes advantages of the prediction property of SMVQ state codebook. In addition, it adaptively adjusts the hiding procedure according to the local feature within the cover image. According to the computer simulation, it is confirmed that the hiding capacity for secret data and the visual quality of the cover images obtained by the proposed scheme is much better than the earlier works. Moreover, the receiver can efficiently receive both the compressed cover image and the hidden secret data simultaneously.

Joint coding and embedding techniques for multimedia images

Hiding data in digital images is a technique for secret data communication. It involves embedding important data into a cover image in digital form with minimal perceptible degradation. In this paper, we first propose an improved data hiding method for side-match vector quantization (SMVQ) compressed images, which utilizes the codeword-pairing procedure to modulate the index code of each image block. In order to take it a step further and obtain a larger embedding capacity, we remodel the first proposed method into a new one with the power of variable-rate data embedding through the use of a multiple-base notational system. According to the experimental results, the two proposed methods are indeed capable of providing a larger embedding capacity without causing noticeable distortions of stego-images in comparison with previous methods. Moreover, our methods also maintain a low compression bit-rate without auxiliary data.

Hiding Secret Information Using Adaptive Side-Match VQ

The major application of digital data hiding techniques is to deliver confidential data secretly via public but unreliable computer networks. Most of the existing data hiding schemes, however, exploit the raw data of cover images to perform secret communications. In this paper, a novel data hiding scheme was presented with the manipulation of images based on the compression of side-match vector quantization (SMVQ). This proposed scheme provided adaptive alternatives for modulating the quantized indices in the compressed domain so that a considerable quantity of secret data could be artfully embedded. As the experimental results demonstrated, the proposed scheme indeed provided a larger payload capacity without making noticeable distortions in comparison with schemes proposed in earlier works. Furthermore, this scheme also presented a satisfactory compression performance.

A Reversible Data Hiding Scheme Based on Side Match Vector Quantization

Many researchers have studied reversible data hiding techniques in recent years and most have proposed reversible data hiding schemes that guarantee only that the original cover image can be reconstructed completely. Once the secret data are embedded in the compression domain and the receiver wants to store the cover image in a compression mode to save storage space, the receiver must extract the secret data, reconstruct the cover image, and compress the cover image again to generate compression codes. In this paper, we present a reversible data hiding scheme based on side match vector quantization (SMVQ) for digitally compressed images. With this scheme, the receiver only performs two steps to achieve the same goal: extract the secret data and reconstruct the original SMVQ compression codes. In terms of the size of the secret data, the visual quality, and the compression rate, experimental results show that the performance of our proposed scheme is better than those of other information hiding schemes for VQ-based and SMVQ-based compressed images. The experimental results further confirm the effectiveness and reversibility of the proposed scheme

An image coding scheme using SMVQ and support vector machines

To efficiently compress images, the spatial relations among image blocks to achieve low bit-rate compression has been widely adopted in these years. An example of using such spatial relations is side-match vector quantization (SMVQ). In this paper, a new technique utilizing support vector machines (SVM) to enhance the quality of images compressed by an algorithm based on SMVQ is proposed. We incorporate SVM as a part of the encoder/decoder to detect the edges across boundaries and, therefore, to further improve the accuracy in the block predicting phase, while the number of codewords available in the state codebooks can be enlarged at the same time without increasing the bit-rate. Our simulation confirms the superiority of the proposed scheme. Reasonable improvements in the resulting images were also obtained.