Vector Quantization Index Research Articles

Lossless Recompression of Vector Quantization Index Table for Texture Images Based on Adaptive Huffman Coding Through Multi-Type Processing

With the development of the information age, all walks of life are inseparable from the internet. Every day, huge amounts of data are transmitted and stored on the internet. Therefore, to improve transmission efficiency and reduce storage occupancy, compression technology is becoming increasingly important. Based on different application scenarios, it is divided into lossless data compression and lossy data compression, which allows a certain degree of compression. Vector quantization (VQ) is a widely used lossy compression technology. Building upon VQ compression technology, we propose a lossless compression scheme for the VQ index table. In other words, our work aims to recompress VQ compression technology and restore it to the VQ compression carrier without loss. It is worth noting that our method specifically targets texture images. By leveraging the spatial symmetry inherent in these images, our approach generates high-frequency symbols through difference calculations, which facilitates the use of adaptive Huffman coding for efficient compression. Experimental results show that our scheme has better compression performance than other schemes.

One-index vector quantization based adversarial attack on image classification

To improve storage and transmission, images are generally compressed. Vector quantization (VQ) is a popular compression method as it has a high compression ratio that suppresses other compression techniques. Despite this, existing adversarial attack methods on image classification are mostly performed in the pixel domain with few exceptions in the compressed domain, making them less applicable in real-world scenarios. In this paper, we propose a novel one-index attack method in the VQ domain to generate adversarial images by a differential evolution algorithm, successfully resulting in image misclassification in victim models. The one-index attack method modifies a single index in the compressed data stream so that the decompressed image is misclassified. It only needs to modify a single VQ index to realize an attack, which limits the number of perturbed indexes. The proposed method belongs to a semi-black-box attack, which is more in line with the actual attack scenario. We apply our method to attack three popular image classification models, i.e., Resnet, NIN, and VGG16. On average, 55.9 % and 77.4 % of the images in CIFAR-10 and Fashion MNIST, respectively, are successfully attacked, with a high level of misclassification confidence and a low level of image perturbation.

An Innovative Recompression Scheme for VQ Index Tables

As we move into the digital era, the pace of technological advancement is accelerating rapidly. Network traffic often becomes congested during the transmission of large data volumes. To mitigate this, data compression plays a crucial role in minimizing transmitted data. Vector quantization (VQ) stands out as a potent compression technique where each image block is encoded independently as an index linked to a codebook, effectively reducing the bit rate. In this paper, we introduce a novel scheme for recompressing VQ indices, enabling lossless restoration of the original indices during decoding without compromising visual quality. Our method not only considers pixel correlations within each image block but also leverages correlations between neighboring blocks, further optimizing the bit rate. The experimental results demonstrated the superior performance of our approach over existing methods.

Adaptive encoding based lossless data hiding method for VQ compressed images using tabu search

A novel joint image coding and reversible data hiding method for vector quantization (VQ) compressed images is proposed in this paper. Since the original VQ indices often exhibit uncorrelated, a rearrangement of them by considering their correlations may benefit the prediction performance so as to reduce the required bitrate. In this study, the tabu search algorithm is employed to rearrange codewords by fully exploiting their neighboring correlations, yielding a moepressible rearranged indices. By combining more highly-correlated indices of a to-be-predicted index into prediction, the improved linear regression method is then applied to achieve a sharper prediction-error histogram and less required additional information. After prediction, an adaptive run-length encoding method is presented to encode prediction errors, thereby eliminating unnecessary indicators. Experimental results demonstrate that the proposed method effectively reduces the bitrate of the compressed image while providing a comparable hiding capacity.

Secure Image Authentication Scheme Using Double Random-Phase Encoding and Compressive Sensing

Double random-phase encoding- (DRPE-) based compressive sensing (CS) systems support image authentication for noisy images. When extending such systems to resource-constrained applications, how to ensure the authentication strength for noisy images becomes challenging. To tackle the issue, an efficient and secure image authentication scheme is presented. The phase information of the plain image is generated using DRPE and quantized into a binary image as the authentication information. Meanwhile, a sparser error matrix generated by the same plain image and vector quantization (VQ) image works as the input of CS. The authentication information and VQ indexes are self-hidden into the quantized measurements to construct the combined image. Then, it is permutated and diffused with the chaotic sequences generated from a modified Henon map. After decryption at the receiver side, the verifier can implement the blind authentication between the noisy decoded image and the reconstructed image. Supported by the detailed numerical simulations and theoretical analyses, the DRPE-CSVQ exhibits more powerful compression and authentication capability than its counterpart.

Two-Layer Reversible Data Hiding for VQ-Compressed Images Based on De-Clustering and Indicator-Free Search-Order Coding

During transmission of digital images, secret messages can be embedded using data hiding techniques. Such techniques can transfer private secrets without drawing the attention of eavesdroppers. To reduce the amount of transmitted data, image compression methods are widely applied. Hiding secret data in compressed images is a hot issue recently. In this paper, we apply the de-clustering concept and the indicator-free search-order coding (IFSOC) technique to hide information into vector quantization (VQ) compressed images. Experimental results show that the proposed two-layer reversible data hiding scheme for IFSOC-encoded VQ index table can hide a large amount of secret data among state-of-the-art methods with a relatively lower bit rate and high security.

Reversible Data Hiding Scheme Based on VQ Prediction and Adaptive Parametric Binary Tree Labeling for Encrypted Images

In this paper, we propose a reversible data hiding scheme for the encrypted images (RDHEI) based on vector quantization (VQ) prediction and parametric binary tree labeling (PBTL). VQ compression is a lossy image compression method, the difference between the original image and the decompressed image is small when the length of codebook is sufficient. Thus, VQ can be applied as a tool for pixel value prediction. Based on VQ prediction, PBTL method is applied to label the embeddable and non-embeddable pixels. Through adaptive setting of parameters, the modified PBTL can provide optimal pixel labeling strategies and thus maximize the overall embedding capacity. Furthermore, the VQ index and the secret data are stream ciphered to avoid leakage of the image content and secret information. Different metrics are used to show that the marked encrypted images are highly secure. In comparison with several state-of-the-art schemes, our scheme outperforms the related works in embedding rate for two commonly applied image databases. In addition, extraction of the secret data and recovery of the original image can be operated separately according to authorization.

Subdata image encryption scheme based on compressive sensing and vector quantization

An advanced image encryption scheme should equip the capability against malicious attacks, reduce the losses under attacks, and improve the compression rate tremendously due to the unsafe network environment and the limited bandwidth resources. Recently, compressive sensing (CS) has been introduced into image encryption schemes because of the merit of low sampling rate. However, these schemes still cannot address the above requirements well. In order to improve compression rate while providing higher security level, a novel subdata image cryptosystem is proposed by introducing vector quantization (VQ) into CS-based encryption scheme. The plaintext image is first divided into VQ index blocks and the error compensations that are sparse enough to be compressed by CS. Then, the index information and CS measurements are further scrambled and diffused by chaotic sequences to achieve enhanced security. It can be ensured that the primary index information is informative and occupies smaller proportion of cipher image such that it cannot be easily tampered if only a part of the image is attacked. In contrast, the secondary error information is a good supplement to the former and occupies larger proportion. Simulation results verify that our proposed scheme has overwhelming compression rate and security effect to resist malicious attacks when compared with the state-of-art schemes. In addition, even if the important information is damaged, the destroyed pixels can be located and the plaintext image can be reconstructed with VQ neighbor indexes.

A low bit-rate SOC-based reversible data hiding algorithm by using new encoding strategies

Search order coding (SOC) benefits a lot from the correlation of neighboring blocks for vector quantization (VQ)-compressed images. SOC selects a number of different indices in its search path as candidate search order codes. In this work, we present a low bit-rate SOC-based reversible data hiding algorithm benefiting from novel encoding strategies which exploit the information of the placeholders. The blocks are classified into two categories by the placeholders, where different encoding strategies are designed, respectively. Firstly, for the block in smooth region, the placeholders in its neighborhood are employed to compress the VQ index. Secondly, for the block in complex region, SOC is employed to compress the VQ index. Finally, for the blocks that cannot be processed by its placeholders and SOC, an effective prediction method named accurate gradient selective prediction (AGSP) and Huffman coding are introduced. After encoding phase, the size of the output bit stream is reduced so that space is saved for data embedding. Experiment results show that our proposed method outperforms other state-of-the-art SOC-based algorithms in bit rate and embedding capacity.

Two reversible data hiding schemes for VQ‐compressed images based on index coding

This study proposes two reversible data hiding (RDH) schemes for vector quantisation (VQ)-compressed images based on switching-tree coding (STC) and dynamic tree-coding scheme (DTCS). Most developed VQ-based RDH schemes produce non-legitimate codes as output. In order to preserve the legitimacy of the embedded VQ code, some schemes embed data into VQ indices by employing an index replacement mechanism and some other schemes perform embedding by adopting one of the possible ways during encoding each index when multiple ways are possible to encode the index. In the current research, two schemes are proposed based on the second mechanism. Outputted code of the proposed schemes is a legitimate STC/DTCS code and the conventional STC/DTCS decoder can decode it to the original VQ index table. The experimental results show that the proposed schemes are feasible and in comparison with some previous RDH schemes, the first one provides higher embedding capacity and the second one embeds a substantial amount of data while provides lower bit rate than most the previous schemes. In addition, the embedding-efficiency of both proposed schemes is higher than that of the previous schemes.

An efficient histogram-based index mapping mechanism for reversible data hiding in VQ-compressed images

In this paper, a reversible data hiding scheme for vector quantization (VQ)-compressed images is proposed. The scheme introduces two methods to explore the high correlations between VQ indices of an index table, followed by exploiting the redundancy elimination for data embedding. The first method utilizes the concept of side matching and the second one uses the feature of locally repetitive occurrence of the VQ indices in VQ-compressed images. In this research, some concepts of information theory are studied to design an efficient index mapping mechanism in the embedding capacity point of view. The proposed index mapping mechanism partitions the positions of the sorted codebook into some intervals, and assigns each interval of the positions to each position with high hit rate in the sorted codebook. Then, based on the secret bits, each index located in the positions of the sorted codebook with high hit rates is mapped to one of the indices in the interval assigned to that position. Furthermore, an interval of the sorted codebook is reserved as the indicator of the indices with low hit rates. The experimental results demonstrate that the proposed scheme significantly outperforms the existing schemes in terms of the embedding capacity and compression-embedding efficiency.

Reversible data hiding for VQ indices using hierarchical state codebook mapping

In this paper, we present an approach to efficiently hide sensitive data in vector quantization (VQ) indices and reversibly extract sensitive data from encrypted code stream. The approach uses two patterns to compress VQ indices. When an index is equal to its upper neighbor’s index or left neighbor’s index, it is encoded by the corresponding equivalent index; otherwise, it is encoded by a modified VQ codebook mapping named as hierarchical state codebook mapping (HSCM). In the proposed scheme, the hierarchical state codebook mapping (HSCM) is main coding pattern and it is generated according to the side-match distortion method(SMD). By the above two patterns, the size of original code stream is reduced, and more storage space can be used to embed sensitive data. The experimental results indicated that the proposed scheme can achieve a higher embedding capacity than the previous state-of-the-art VQ-index-based data hiding methods.

VQ-based data hiding in IoT networks using two-level encoding with adaptive pixel replacements

Internet of things (IoT) realizes the concept of bringing things connected together. Data are exchanged and controlled within one or more IoT networks. Sensitive data transferred between different IoT networks may also lead to data leakage. One way to reduce the risk of these problems is to employ steganography while delivering secret information over the IoT networks. This paper presents a steganographic scheme that employs vector quantization (VQ) transformation and the least significant bits (LSB) to embed secret data into a cover image. We devise a new technique, namely two-level encoding, to separate the pixels of a \(4\times 4\) VQ-transformed image block into two groups, the LSB group and the secret data group, in the first level. Then we use an indirect approach that embeds VQ indexes in the LSB group and secret data in the secret data group in the second level. The embedded VQ indexes are used to represent the VQ-transformed image blocks, and the secret data are used as the difference values to adjust the VQ-transformed image blocks and to create stego-image blocks, such that the stego-image blocks become more similar to the original image blocks after embedding. Compared with other similar work, the experimental results show that the proposed scheme produces stego-images with slightly better quality in terms of PSNR; the experimental results also indicate that it provides about ten times as large as the embedding capacity of the prior similar schemes. Moreover, the proposed scheme is able to pass the popular detections, such as Chi-square test and AUMP LSB, both to detect whether an image uses LSB for data hiding.

Image retrieval based on histograms of EOPs and VQ indices

A new feature for image retrieval based on edge orientation patterns (EOPs) by classifying image blocks into nine classes according to their EOs. Each input colour image is decomposed into Y, Cb and Cr components, and each component image is divided into non-overlapping 4 × 4 blocks. For each block, eight EO templates of size 4 × 4 are performed to determine its EO. On the other hand, vector quantisation (VQ) is an efficient clustering and classification technique for high-dimensional spaces, and thus VQ is also used to extract the texture features. The retrieval simulation results show that the EOP-histogram-based features combined with the traditional VQ-index-histogram-based features can largely improve the recall and precision performance.

Reversible data hiding in VQ index table with lossless coding and adaptive switching mechanism

In recent years, reversible data hiding (RDH) has attracted extensive attention from the academia. Besides RDH for uncompressed images, RDH for compressed images with satisfactory embedding performance is also important due to the universal use of image compression. In this paper, a RDH scheme for the encoded Vector Quantization (VQ) index table through the Improved Searching Order Coding (ISOC) is proposed. Original image is first compressed with a sorted VQ codebook and further losslessly encoded by the ISOC technique to produce an index table, in which all indices are categorized into three types, i.e., SOC type, Relative Addressing (RA) type and VQ type, based on the relationship between the index and its neighborhood. Each of the first two types of indices is adaptively switched or unchanged according to the current secret bit to be embedded, and the third type of indices isn't used to embed secret data. Experimental results show that, our scheme can embed secret data into 78.396% and 78.377% of all image blocks when VQ codebook sizes equal 256 and 1024, respectively. Compared with the reported schemes, our scheme can achieve higher hiding capacity with lower bit rate and extension degree of index table.

Fragile image watermarking scheme based on VQ index sharing and self-embedding

In this paper, we propose a self-embedding fragile watermarking scheme using vector quantization (VQ) and index sharing. First, the principle contents of original image are compactly represented by a series of VQ indices. Then, after permutation, the binary bits of VQ indices are extended to generate reference-bits by a random binary matrix, which can make all reference-bits share the information of VQ index bits from different regions of the whole image. The image is embedded with watermark-bits including hash-bits for tampering localization and reference-bits for content recovery, and is transmitted to receiver side. Tampered regions in the received, suspicious image can be accurately located and then be recovered by VQ index reconstruction. Experimental results demonstrate that the proposed scheme can achieve successful content recovery for larger tampering rate and obtain better visual quality of recovered results than the reported schemes.

A Lossless Data Hiding Scheme for VQ Indexes Based on Joint Neighboring Coding

Designing a new reversible data hiding technique with a high embedding rate and a low compression rate for vector quantization (VQ) compressed images is encouraged. This paper proposes a novel lossless data hiding scheme for VQ-compressed images based on the joint neighboring coding technique. The proposed method uses the difference values between a current VQ index and its left and upper neighboring VQ indexes to embed n secret bits into one VQ index, where n = 1, 2, 3, or 4. The experimental results show that the proposed scheme achieves the embedding rates of 1, 2, 3, and 4 bits per index (bpi) with the corresponding average compression rates of 0.420, 0.483, 0.545, and 0.608 bit per pixel (bpp) for a 256 sized codebook. These results confirm that our scheme performs better than other selected reversible data hiding schemes.

A reversible steganographic scheme for VQ indices based on joint neighboring and predictive coding

In this paper, we propose a novel reversible steganographic technique to embed secret data into digital images compressed using vector quantization (VQ). The proposed method is based on joint neighboring and predictive coding. The proposed technique can embed n secret bits into one VQ index, where nź=ź1, 2, 3, and 4. Our method uses left and upper neighboring VQ indexes and the difference between the current VQ index and the predicted value produced by the median edge detector predictor to achieve a low bit rate. The experimental results show that the proposed approach obtains embedding rates of 1, 2, 3, and 4 bits per index (bpi) with respective average bit rates of 0.409, 0.471, 0.534, and 0.596 bit per pixel (bpp) for a 256 sized codebook. This confirms that the proposed scheme outperforms three similar reversible data hiding schemes in VQ-compressed domain.

Automatic sleep staging based on ECG signals using hidden Markov models.

This study is designed to investigate the feasibility of automatic sleep staging using features only derived from electrocardiography (ECG) signal. The study was carried out using the framework of hidden Markov models (HMMs). The mean, and SD values of heart rates (HRs) computed from each 30-second epoch served as the features. The two feature sequences were first detrended by ensemble empirical mode decomposition (EEMD), formed as a two-dimensional feature vector, and then converted into code vectors by vector quantization (VQ) method. The output VQ indexes were utilized to estimate parameters for HMMs. The proposed model was tested and evaluated on a group of healthy individuals using leave-one-out cross-validation. The automatic sleep staging results were compared with PSG estimated ones. Results showed accuracies of 82.2%, 76.0%, 76.1% and 85.5% for deep, light, REM and wake sleep, respectively. The findings proved that HRs-based HMM approach is feasible for automatic sleep staging and can pave a way for developing more efficient, robust, and simple sleep staging system suitable for home application.

New high-performance reversible data hiding method for VQ indices based on improved locally adaptive coding scheme

In this paper, a new high-performance reversible data hiding method for vector quantization (VQ) indices is proposed. The codebook is firstly sorted using the unidirectional static distance-order technique to improve the correlation among the neighboring indices. The two-dimensional structure of image and the high correlation among the neighboring blocks are used to update the self-organized list L in the improved locally adaptive coding scheme (ILAS). Then a new embedding rule according to the complexity of the region at which the current block locates and the position of current block index in the list L is proposed to obtain a better embedding capacity. The experimental results demonstrate that our proposed method has a better performance in terms of compression rate, embedding capacity and embedding rate compared with the related data hiding methods.