Side-match Vector Quantization Research Articles

Histogram Shifting-Based Quick Response Steganography Method for Secure Communication

Steganography is a tool which allows the data for transmission by concealing secret information in a tremendously growing network. In this paper, a novel technique quick response method (QRM) is proposed for the purpose of encryption and decryption. Existing system uses side match vector quantization (SMVQ) technique which has some challenges such as security issues and performance issues. To handle the security and performance issues, the proposed system uses two methods, namely, quick response method and shifting method. In the proposed system, encoding part calculates the performance for capacity, PSNR (peak signal-to-noise ratio), MSE (mean square error), and SSIM (structural similarity index method), and the decoding part calculates the performance of MSE (mean square error) and PSNR (peak signal-to-noise ratio). The shifting method is used to increase the data hiding capacity. In this system, the encryption part embeds the secret image using steganography and the decryption part extracts the original image. By analyzing and comparing the proposed system with the existing system, it is proved that the system proposed was much better than the existing systems.

WITHDRAWN: A novel joint data-hiding and compression scheme based on SMVQ and image inpainting

For the advanced digital pictures an innovative joint data concealing and compression methodology is proposed in our system. This is achieved by utilizing side match vector quantization (SMVQ) and image inpainting. Information stowing away and compression are the two parts which can be integrated into a single module cleanly. Apart from the squares within the furthest left and the top of the image, all the remaining squares in the raster-examining request can be inserted with mystery data and compacted all the while by SMVQ or image encoding adaptively as per the present inserting bit on the sender facet. To regulate the visual twisting and fault dispersion vector quantization is additionally used for mind boggling squares. These two are brought by the liberal compression. The receiver can accomplish the extraction of mystery pieces and picture decompression effectively as per the index estimates in the segmented areas. This is subsequent to dividing the image packed codes into a progression of segments by the marker bits.

A Strategy for the Linear Time Implementation of the SMVQ Algorithm

This paper proposes a linear time implementation of the Side Match Vector Quantization (SMVQ) algorithm. It maps the side match distortion values between codewords and a boundary vector onto an order-preserving integer sequence. Hence the SMVQ algorithm’s state codebook generation step can exploit any integer sorting algorithm to improve its overall runtime complexity. The proposed SMVQ implementations utilize the bucket sort algorithm to demonstrate this concept. The experimental results show a percentage change of −84.26% for N = 1024 (46.052–7.247 s) and −94.82% for N = 4096 (998.355–51.710 s) in the average execution time of the SMVQ algorithm.

Enhanced imperialist competitive algorithm based efficient reversible data hiding technique

This paper presents a novel reversible data hiding into a Vector Quantization (VQ) and Side Match Vector Quantization (SMVQ) based compression image to embed high capacity secret bits and recover cover image after data extraction. For optimal embedding capacity and to achieve exact recovery of cover image, this paper uses Enhanced Imperialist Competitive Algorithm (EICA). The threshold value is determined by the fitness function contrast sensitivity in EICA in order to signify embedding rate of each region in a cover image based on the size of the secret message. During data hiding, the output size of code stream is preserved in hiding two secret bits into a single index value. Discrete Cosine Transform (DCT) and Burrows Wheeler Transform (BWT) is applied before quantization for exact recovery of cover image and to achieve high compression ratio. Excellent energy compaction is provided by DCT and BWT reorders the symbols according to their context. Thus the proposed method provides a novel technique to embed secret bits into the cover image and compresses the embedded image. The output will be in the form of code streams with preserved size. The experimental results show that the proposed technique achieves high embedding capacity and compression rate.

A centroid based vector quantization reversible data hiding technique

In this paper, we propose a reversible data hiding scheme that exploits the centroid formula. Specifically, we use it to define a centroid boundary vector and a centroid state codebook CSCB. Initially, our centroid boundary vectors and CSCBs are the same as the side match vector quantization (SMVQ) algorithm’s boundary vectors and state codebooks SCBs. For each VQ index, the proposed scheme exploits the centroid formula to update its centroid boundary vector and the corresponding CSCB. The updating is coupled with a heuristic to select the best state codebook (i.e., either SCB or CSCB) for each VQ index, which generates a highly compressible distribution of index values. Our experimental results show that the proposed scheme can embed n = 1, 2, 3, and 4 bit per index (bpi) at bit rates of 0.332, 0.394, 0.457, and 0.519 bit per pixel (bpp), respectively, for the main codebook size N = 256. These results confirm that the proposed scheme improves recent VQ and SMVQ based reversible data hiding schemes.

A scheme of flexible and reversible information-hiding in SMVQ mechanism

Information hiding refers to concealing data into cover media to prevent the private message from being stolen in peer-to-peer networks, mobile communication, and the cloud environment. Vector quantization (VQ) provides one means for this purpose; a distortion is created between the raw media and VQ compressed media. In this study, we proposed a flexible side-match vector quantization (SMVQ) information-hiding method. By using this method, the private message can be embedded into the distortion, and the visual quality of the stego media is improved. The proposed method achieves an improvement of about 13.13% (i.e., +3.73 dB) in the peak signal-to-noise ratio of the stego image and an improvement of about 59,355 bits of embedding capacity on average compared with Wang et al.’s method. The experiment results verify the significant contributions of this study to the field of SMVQ-based information hiding, especially with regard to the image quality of the stego image and the embedding capacity.

A novel low bit rate side match vector quantization algorithm based on structed state codebook

Side match vector quantization (SMVQ) is a widely used image compression algorithm for data hiding applications. Compared with conventional vector quantization (VQ) algorithm, a smaller and more powerful state codebook (SC) which is generated by utilizing the correlation in natural image is used in SMVQ to achieve low bit rate. However, the visual quality of reconstructed image by using SMVQ is significantly decreased. In this paper, a novel low bit rate coding algorithm named structured SMVQ (SSMVQ) is proposed. The size of SSMVQ’s SC is flexible and the SC of SSMVQ is composed by a smaller SC of conventional SMVQ and a supporting codebook which is newly introduced in this paper. Experimental results show that the proposed structed SMVQ is able to achieve satisfactory PSNR when the bit rate is extremely low.

A hybrid encoding method for reducing code stream in EICA-optimized SMVQ reversible data hiding

An enhanced imperialist competitive algorithm (EICA)-optimized side-match vector quantization (SMVQ)-based reversible data hiding (VQ-SMVQ with EICA) was developed for embedding higher number of secret bits. This technique improved the embedding capacity rate of compression and quality of reconstruction. However, for improving security and minimizing the length of the output code stream left-upper coding (LUC) was along with SMVQ, since LUC algorithm is more appropriate for compression of SMVQ index table. In this paper, the code stream size is reduced by proposing an efficient encoding approach. Initially an improved locally adaptive coding (ILA) scheme is used for effective distribution of the position of SMVQ indices based on adaptive threshold value. A locally adaptive data compression scheme (LAS) is utilized with IAS to improve the compression rate of secret data simultaneously with embedding capacity. The combined technique is achieved highly reduced length of output code stream. Thus, EICA-optimized SMVQ reversible data hiding with hybridized ILA and LAS outperforms than existing schemes.

Efficient image compression based on side match vector quantization and digital inpainting

In this paper, we propose two efficient compression schemes for digital images using an adaptive selection mechanism for vector quantization (VQ), side match vector quantization (SMVQ), and image inpainting. On the sender side, after the original image is divided into blocks, the compression is implemented block by block. In both schemes, blocks in pre-specified locations are first compressed by VQ. For each remaining block, the optimal compression method (for the first scheme, including VQ or inpainting, and for the second scheme, including VQ, SMVQ, and inpainting) is determined by computing the mean square error (MSE) between the original block and its inpainted result and then comparing it with a predefined threshold. If MSE is greater than the threshold, image inpainting continues to be used to compress the current block. Otherwise, the compression mode of VQ or SMVQ is selected to substitute image inpainting to maintain higher visual quality. With the assistance of transmitted indicator flags, the receiver side can execute the image inpainting and decompression successfully. Experimental results demonstrate the effectiveness and superiority of two proposed schemes.

New SMVQ scheme with exactly the same PSNR of VQ by introducing extend state codebook

Side match vector quantization (SMVQ) is an effective coding technique and it has been widely used in low bit rate image compression and data hiding techniques. It utilizes the correlations between neighboring blocks to better generate a small fixed-size state codebook for each input vector. However, compared with vector quantization (VQ) scheme, the quality of SMVQ-coded image is substantially decreased when bit rate (BR) becomes low. This paper proposes a new side match vector quantization scheme which is based on using extend state codebook (ESCSMVQ). Experimental results show that comparing to conventional SMVQ, the bit rate of ESCSMVQ can be significantly reduced meanwhile keeping the exactly same quality of VQ-coded image.

An Integrated Reversible Data Hiding and Compression Using Enhanced Imperialist Competitive Algorithm Optimized Side-Match Vector Quantization and Modified FoE Inpainting

An Integrated Reversible Data Hiding and Compression Using Enhanced Imperialist Competitive Algorithm Optimized Side-Match Vector Quantization and Modified FoE Inpainting

Rapid Generation of the State Codebook in Side Match Vector Quantization

Rapid Generation of the State Codebook in Side Match Vector Quantization

An Adaptive Data Hiding System Based on SMVQ

Based on Side-Match Vector Quantization (SMVQ), the authors propose two schemes to embed more secret information without significantly sacrificing the compression ratio. The data transform scheme improves the embedding capacity while maintaining the same compression ratio. The direct embedding scheme improves the embedding capacity at the expense of increasing the compression ratio slightly. According to various demands of a user, the integrated system can adaptively select a suitable method from the proposed ones. Experimental results show that the adaptive system provides the users with full flexibility and preserves a balance between the embedding capacity and compression ratio.

Enhanced side match vector quantisation based on constructing complementary state codebook

Side match vector quantisation (SMVQ) technique has been widely used in a lot of image compression and data hiding applications. It can effectively decrease the bit rate (BR) but the encoding visual quality of the image using SMVQ is generally poor because the correlation among neighbouring image blocks is still low. In this study, the authors propose a new enhanced SMVQ (ESMVQ) by introducing the concept of complementary state codebook (CSC) to improve the visual quality of SMVQ. Encoding input vectors by using CSC, ESMVQ can achieve almost the same encoding visual quality as using the conventional vector quantisation. As a result, the encoding visual quality of image can be significantly improved by exploiting the power of CSC. Experimental results show that the improvement of proposed ESMVQ method over SMVQ is up to 4.677 dB at a similar BR for image Lena when the main codebook size is 1024.

Reversible data hiding scheme for VQ indices based on modified locally adaptive coding and double-layer embedding strategy

In this paper, a new reversible data hiding scheme for vector quantization (VQ) compressed images is proposed. The proposed scheme utilizes the modified locally adaptive coding scheme which is initially made up with the codebook indices to guarantee that all VQ indices can be coded using the self-organized list L. Then the side match vector quantization (SMVQ) approach is used to improve the performance of locally adaptive coding scheme and a lossless coding method is brought into encode the positions at which the VQ indices appear in the list L. Finally a double-layer embedding strategy, which embeds the secret message into two layers that are the block type descriptor and the encoded position value, is proposed to obtain a better embedding ability. The experimental results demonstrate that our proposed scheme has a better performance in terms of compression rate, embedding payload and embedding efficiency compared with the related schemes.

A reversible compression code hiding using SOC and SMVQ indices

Reversible data hiding recovers the original images without distortion after the secret data are extracted, so it has received considerable attention among researchers in recent years. This work presents a novel reversible data hiding scheme based on the search-order coding (SOC) algorithm and side match vector quantization (SMVQ). The compression performance is improved by encoding the index with either SOC or SMVQ during the data embedding phase. The index of an image block also is encoded by using a few bits, thus improving the compression rate. During the extracting phase, the receiver uses simple steps to extract the secret data and reconstruct the original SMVQ compression code. Our experimental results indicated that, using a size 64 state codebook, the average compression rate with our scheme was 0.41bpp, which is much better than the compression rates of Chang et al.’s, Yang and Lin’s, and Yang et al.’s schemes, i.e., 0.50, 0.54, and 0.56bpp, respectively. Furthermore, in addition to reducing the time required for the embedding and extracting phases, the proposed scheme yields a higher embedding rate than the schemes of Yang and Lin and Yang et al.

English

Data hiding has a vital role to play in information security. Using a data hiding technique, secret information is hidden into cover digital content. Compression techniques and data hiding techniques received much less attention from the research community and from industry than cryptography. In past research many data hiding and image compression techniques has been developed, it works independent module in both sender and server side. These method cause low efficiency, less security and also the low bit rate scheme requires more time to encode. Both image compression and data hiding (EJDHC) using residual codebooks with side match vector quantization (SMVQ). In this survey is to discuss on data hiding schemas and compression techniques such as JPEG, JPEG 2000, vector quantization, VQ compression and SMVQ.

Large capacity and high quality reversible data hiding method based on enhanced side match vector quantization

In recent years, many reversible data hiding techniques have been proposed to solve information security problem. In this paper, we propose a novel reversible data hiding method based on enhanced side-match vector quantization (ESMVQ). We embed as many secret bits into the images compressed by ESMVQ which uses the very small state codebook size of 4 to achieve a large hiding capacity and meanwhile ensure the compressed image quality by introducing a complementary state codebook. The experimental results demonstrate that the proposed method has a large embedding capacity and a good image quality. The experimental results also show that our proposed method outperforms the methods of Chang et al. (IEEE Trans on Circ and Syst for Vi Techno 16(10):1301---1308, 2006), Huang et al. (The Image Sci J 61(2):195---203, 2013), Lee et al. (IEEE Trans on Inf Forensic and Secur 5(4):638---648, 2010) and Wang et al. (Inf Sci 246:69---82, 2013).

Information Hiding Based on Block Match Coding for Vector Quantization-Compressed Images

In this paper, a novel highly efficient lossless data hiding scheme is proposed to achieve the goal of hiding secret data into vector quantization (VQ)-compressed images that can be losslessly reconstructed after the secret data is extracted in the decoder. For VQ-compressed images, the index-modifying and the side-match VQ (SMVQ) techniques can be applied to encode indices and hide secret data. In general, data hiding accompanied by the SMVQ technique can yield a higher embedding capacity and a lower bit rate, yet more time consuming. In contrast, data hiding accompanied by the index-modifying technique can yield a lower embedding capacity and a higher bit rate, yet less time consuming. To possess the advantages of the two techniques while removing the shortcomings, the neighboring processed indices are employed to speed up the processes of generating state codebooks required for encoding and hiding. To evaluate the effectiveness of this approach, various test images are employed in the experiments. As documented in the experimental results, it is shown that the performance of the proposed scheme is superior to former schemes in terms of compression ratio, embedding rate, processing efficiency, and embedding capacity.

English

In any communication, security is the most important issue in today’s world. Security and data hiding algorithms have been developed, which worked as motivation for the research. This project is a data hiding by using side match vector quantization algorithm may used for data hiding scheme, which provides a strong backbone for its security. The scenario of present day of information security system includes privacy, genuineness, honesty, non-repudiation. This present work focus is hiding for data technique to secure data or message with authenticity and truthfulness. In this project work, the secret message is encrypted before the actual embedding process starts. The entire work has done in MATLAB. The hidden message is encrypted using a simple encryption algorithm using secret key for SMVQ and hence it will be almost unfeasible for the impostor to unhide the actual secret dispatch from the embedded cover file without knowing furtive key. Only receiver and sender know the secret key. SMVQ substitution technique is used as embedding and extraction method. We propose that this method could be most appropriate for hiding any secret message (text, image, audio, and video) in any standard cover media such as image, audio, video files.