Vector Quantization Index Research Articles

Lossless data hiding for VQ indices based on neighboring correlation

Data hiding is one of the protective techniques for authentication or secret communication through a public and open channel such as the Internet. In this study, a novel lossless data hiding by embedding secret data into a Vector Quantization (VQ)-compressed image is proposed to achieve secret communication and data compression simultaneously. The correlation of neighboring blocks of a VQ-compressed image is explored. It is shown that the neighboring blocks of a VQ-compressed image normally have high mutual correlation. Thus, this scheme employs the neighboring processed compression indices to generate specific sub-codebooks required for encoding and hiding data simultaneously. Since the sizes of sub-codebooks are smaller than that of the original VQ codebook, the encoded size of each index can be significantly reduced. As a result, a great deal of extra free space can be created. Moreover, the original VQ-compressed images can be perfectly recovered after data extraction. 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 the former schemes in terms of compression ratio, embedding rate, execution time, and embedding capacity.

A High-Capacity Data Hiding Scheme by Vector Quantization Index with Search-Order Coding

A High-Capacity Data Hiding Scheme by Vector Quantization Index with Search-Order Coding

Data hiding for vector quantization images using mixed-base notation and dissimilar patterns without loss of fidelity

Data hiding without loss of fidelity (also called reversible data hiding) allows an original image to be restored completely after extraction of hidden data embedded in a cover image. In this paper, we propose a reversible scheme based on mixed base notation and dissimilar pattern strategies for vector quantization (VQ) compressed images. The dissimilar patterns resulting from the declustering process make the proposed hiding method more efficient. Our experimental results show that the time required for the embedding process in the proposed method is much less than that in the side-matched VQ (SMVQ)-based reversible method. In addition, the use of declustering can reduce many more prediction errors than traditional clustering methods. The results also show that the number of declustered groups and the spatial characteristics of the cover image will affect the embedding capacity and the size of the stego VQ index table.

Vector quantisation index compression based on a Coding Tree Assignment Scheme with Improved Search-Order Coding algorithms

This study proposes a Coding Tree Assignment Scheme with Improved Search-Order Coding algorithms (CTAS-ISOC) to enhance the coding efficiency of the original SOC by exploiting the correlations of the neighbouring blocks using the left-pair and upper-pair patterns in the index domain. The essential techniques consist of three major elements: the Neighbouring Index Code Assignment (NICA), the Left-pair Search-Order Coding (LSOC) and the Upper-pair Search-Order Coding (USOC). The NICA approach assigns a short code to the current index by using the statistics on the indices of the neighbouring blocks. The LSOC (USOC) compares the current left (upper) index pair with previous index pairs in a predefined search path. Since the predefined search path is exploited with a correlation viewpoint, both LSOC and USOC achieve better compression than the original SOC. Experimental results demonstrate the effectiveness of the proposed scheme in comparison with some existing popular lossless index coding schemes.

Huffman-code strategies to improve MFCVQ-based reversible data hiding for VQ indexes

Data hiding, which embeds secret data into cover media, is one type of technology used to achieve the multimedia security. A reversible data hiding method has the characteristic that the cover media can be completely reconstructed after secret data are extracted. Recently, some reversible data hiding schemes have focused on the vector quantization (VQ)-compressed format. Using the modified fast correlation vector quantization (MFCVQ) concept, Lu et al. proposed a reversible data hiding scheme for VQ-index tables. In this paper, a new MFCVQ-based scheme is proposed. Specifically, our method will enlarge the embedding capacities by embedding multiple bits into a VQ index, thereby reducing the compressed bit rates by applying the Huffman-code concept and the 0-centered classification. Experimental results indicate that this method has greater pure embedding capacities and fewer compressed bit rates than that of previous MFCVQ-based methods.

Reversible Data Hiding Based on Histogram Modification of SMVQ Indices

This work presents a novel reversible data-hiding scheme that embeds secret data into a transformed image and achieves lossless reconstruction of vector quantization (VQ) indices. The VQ compressed image is modified by the side-matched VQ scheme to yield a transformed image. Distribution of the transformed image is employed to achieve high embedding capacity and a low bit rate. Moreover, three configurations, under-hiding, normal-hiding, and over-hiding schemes, are utilized to improve the proposed scheme further for various applications. Experimental results demonstrate that the proposed scheme significantly enhances the compression ratio and embedding capacity. Experimental results also show that the proposed scheme achieves the best performance among approaches in literature in terms of the compression ratio and embedding capacity.

An encoding method for both image compression and data lossless information hiding

Reversible image data hiding technology means the cover image can be totally recovered after the embedded secret data is extracted. In this paper, we propose a reversible image data hiding scheme based on vector quantization (VQ) compressed images. The secret bits are embedded into the VQ index table by modifying the index value according to the difference of neighboring indices. The data hiding capacity and the size of the final codestream (embedded result) are a trade-off, and it can be decided by users. In other words, the proposed scheme has flexible hiding capacity ability. To estimate the performance, the proposed scheme was compared with the scheme proposed by Wang and Lu (2009). The results of the comparison showed that our proposed scheme is superior to the scheme proposed by Wang and Lu in both data hiding capacity and bit rate.

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.

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.

Global Motion Representation of Video Shot Based on Vector Quantization Index Histogram

In this Letter, we propose a novel method of low-level global motion feature description based on Vector Quantization (VQ) index histograms of motion feature vectors (MFVVQIH) for the purpose of video shot retrieval. The contribution lies in three aspects: first, we use VQ to eliminate singular points in the motion feature vector space; second, we utilize the global motion feature vector index histogram of a video shot as the global motion signature; third, video shot retrieval based on index histograms instead of original motion feature vectors guarantees the low computation complexity, and thus assures a real-time video shot retrieval. Experimental results show that the proposed scheme has high accuracy and low computation complexity.

Color Image Retrieval Based on Distance-Weighted Boundary Predictive Vector Quantization Index Histograms

This Letter proposes a new kind of features for color image retrieval based on Distance-weighted Boundary Predictive Vector Quantization (DWBPVQ) Index Histograms. For each color image in the database, 6 histograms (2 for each color component) are calculated from the six corresponding DWBPVQ index sequences. The retrieval simulation results show that, compared with the traditional Spatial-domain Color-Histogram-based (SCH) features and the DCTVQ index histogram-based (DCTVQIH) features, the proposed DWBPVQIH features can greatly improve the recall and precision performance.

Reversible information hiding for VQ indices based on locally adaptive coding

Steganography is one of protective methods for secret communications over public networks such as the Internet. This paper proposes a novel reversible information hiding method for vector quantization (VQ) compressed images based on locally adaptive coding method. The proposed steganographic method embeds a secret message into VQ indices in an index table during the process of compressing the index table in the block-by-block manner. The experimental results show that, in average, the proposed method achieves the best visual quality of reconstructed images and the best embedding rate compared to two related works. In terms of compression rate and encoding execution time, in average, Yang et al.’s method is the best, followed by our proposed method, and then Lin and Chang’s method.

A Novel High Capacity Reversible Information Hiding Scheme Based on Side-Match Prediction and Shortest Spanning Path

In recent years, information hiding techniques have attracted increased attention owing to the growing demand for the ability to exchange large amounts of information. In addition, hiding information in compressed messages is very practical in improving transmission efficiency and saving large amounts of storage space. However, the damage caused by hiding data in compressed messages reduces image quality more than the damage caused by hiding data in uncompressed messages. Hence, this paper proposes a data hiding scheme based on side-match prediction and declustering techniques for vector quantization (VQ) compressed images. The appropriate side-match prediction not only embeds secret information into the VQ index but also reverses the original compressed index. In addition, the capacity for secret information can be varied adaptively depending on the requirements of different applications. As experimental results indicate, the proposed scheme indeed outperforms previous work in hiding capacity and in the image quality of stego-images.

Image vector quantization codec indices recovery using Lagrange interpolation

Vector quantization (VQ) which has been widely used in the field of video and image coding is an efficient coding algorithm due to its fast decoding efficiency. Sometimes, the indices of VQ will be lost because of the signal interference during the transmission. In this paper, we propose an efficient estimation method by using the Lagrange interpolation formula to conceal and recover the lost indices on the decoder side instead of re-transmitting the whole image again. If the image or video has the limitation of the period of validity, re-transmitting the data wastes of time and occupies the network bandwidth. Therefore, using the received correct data to estimate and recover the lost data is efficient in time constraint situation such as network conference or mobile transmission. For nature images, the pixels with its neighbors are correlative and VQ partitions the image into sub-blocks and quantize them to form the indices to transmit, the correlation between adjacent indices is very strong as well. There are two parts of the proposed method. The first one is preprocessing and the other is the estimation process. In preprocessing, we modify the order of code-vectors in the VQ codebook to increases the correlation among the neighboring vectors. In the second part, using the Lagrange interpolation formula to constitute a polynomial to describe the tendency of VQ indices and use the polynomial to estimate the lost VQ indices on the decoder side. Using conventional VQ to compress Lenna and transmit without any index lost can achieve the PSNR of 30.154 dB on the decoder. The simulation results demonstrate that our method can efficient estimate the indices to achieve the PSNR value of 28.418 dB when the lost rate is 5% and 29.735 dB at the lost rate of 1%.

Multipurpose speech watermarking based on multistage vector quantization of linear prediction coefficients

To make speech watermarking achieve both copyright protection and integrity verification, a novel multipurpose speech watermarking algorithm based on the multistage vector quantization (MSVQ) of linear prediction coefficients (LPCs) is presented in this article. The property of natural speech that the vector quantization (VQ) indices of the LPCs amongst neighboring frames tend to be very similar is utilized to embed the robust watermark in the indices of the first-stage VQ(VQ1). Then, the semi-fragile watermark is embedded in the indices of the second-stage VQ(VQ2) with index constrained VQ encoding scheme. Both the robust watermark and the semi-fragile watermark can be extracted without host speech. Simulation results verify the effectiveness of the proposed algorithm in terms of robustness and semi-fragility.

Robust speech watermarking algorithm

A zero-watermarking method based on vector quantisation (VQ) of linear prediction coefficients (LPCs) is proposed. The characteristic of natural speech that the VQ indices of LPCs among neighbouring frames tend to be very similar is utilised to generate the secret key. Simulation results demonstrate excellent imperceptibility and robustness of our algorithm.

3D Model Retrieval Based on Vector Quantisation Index Histograms

This paper proposes a novel technique to retrieval 3D mesh models using vector quantisation index histograms. Firstly, points are sampled uniformly on mesh surface. Secondly, to a point five features representing global and local properties are extracted. Thus feature vectors of points are obtained. Third, we select several models from each class, and employ their feature vectors as a training set. After training using LBG algorithm, a public codebook is constructed. Next, codeword index histograms of the query model and those in database are computed. The last step is to compute the distance between histograms of the query and those of the models in database. Experimental results show the effectiveness of our method.

Effective correlation vector quantisation algorithm and its VLSI architecture

A correlation-inheriting vector quantisation (VQ) image coding algorithm is presented to re-encode the output indices of VQ after analysing the correlation inheritance of the indices' neighbourhood. Simulation results indicate that this algorithm can compact the VQ index to achieve an ∼21:1 compression ratio on average. In accordance with this new algorithm, an efficient very large scale integration architecture is also derived that, after synthesis, achieves a system clock rate of 110 MHz using a 0.35 µm complementary metal-oxide-semiconductor standard library.

인덱스 변환 함수를 이용한 벡터 양자화 기반의 견고한 다중 워터마킹 방법

In this paper, we propose a robust multi-watermarking method based on vector quantization using an index transform function. In contrast with the conventional watermark embedding methods to embed only one watermark at a time into the original image, we present a method to embed multiple watermarks for copyright protection. The proposed method efficiently enhances the robustness by index transform function which minimizes changes of vector quantization indices against various attacks. Experimental results show that the proposed method has a good robustness against various attacks compared with the conventional multi-watermarking method based on vector quantization.

VQ-based watermarking scheme with genetic codebook partition

To enhance the traditional vector quantisation (VQ) system by adding the watermarking ability, a digital image watermarking scheme, which modifies the VQ indices to carry watermark bits, is presented. This scheme partitions the main codebook into two sub-codebooks by referring to the user-key. Then, for each input vector of the cover image, a sub-codebook is selected according to the watermark bit to be embedding. The traditional VQ coding procedure is then done using the sub-codebook for the vector. Furthermore, to improve the performance of the scheme, a genetic codebook partition (GCP) procedure, which employs the genetic algorithm (GA) to find a better way to split the codebook, is proposed. It is demonstrated that the proposed methods provide faster encoding time, better imperceptibility, stronger robustness under some common attacks, and easier implementation than some related VQ-based watermarking schemes proposed in the literature.