State Codebook Research Articles

Barriers, facilitators, and solutions to familial hypercholesterolemia treatment.

BackgroundFamilial hypercholesterolemia (FH) is an inherited lipid disorder that confers high risk for premature cardiovascular disease but remains undertreated. Causes are multifactorial and multilevel, ranging from underprescribing (at the clinician-level) to medication nonadherence (at the patient-level). We evaluated patient and clinician stakeholder barriers and facilitators for treatment of FH to explore possible solutions to the problem.Methods and resultsSemi-structured interviews and focus groups guided by the Practical, Robust, Implementation and Sustainability Model (PRISM), were conducted with 33 patients and 17 clinician stakeholders across three healthcare systems. A total of14 patients and 9 clinician stakeholders participated in on-site focus groups and the remainder were individual interviews. Transcripts were coded using an iterative process to create a static codebook. We characterized patient and clinician stakeholder barriers into three categories: medical care-, medication-, and life-related. Feasibility of brainstormed solutions varied and was not always representative of the needs of all stakeholders. Patients suggested a need for childhood screening for FH and doctors being persistent about the importance of treating FH, creation of a patient peer group, data transparency, advocacy, and policy changes that would enable patients to receive better treatment. Clinician stakeholders suggested the need for clinical champions. Both groups of stakeholders discussed the need for education about FH.ConclusionsProposed solutions to improve treatment of FH proffered by participants in this study included resources for both patients and clinician stakeholders that clarify cardiovascular disease risks from FH, develop programs to screen for and identify FH at younger ages, and foster open conversations between patients and clinicians about treatment.

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.

Metaheuristic-based vector quantization approach: a new paradigm for neural network-based video compression

Video compression has great significance in the communication of motion pictures. Video compression techniques try to remove the different types of redundancy within or between video sequences. In the temporal domain, the video compression techniques remove the redundancies between the highly correlated consequence frames of the video. In the spatial domain, the video compression techniques remove the redundancies between the highly correlated consequence pixels (samples) in the same frame. Evolving neural-networks based video coding research efforts are focused on improving existing video codecs by performing better predictions that are incorporated within the same codec framework or holistic methods of end-to-end video compression schemes. Current neural network-based video compression adapts static codebook to achieve compression that leads to learning inability from new samples. This paper proposes a modified video compression model that adapts the genetic algorithm to build an optimal codebook for adaptive vector quantization that is used as an activation function inside the neural network’s hidden layer. Background subtraction algorithm is employed to extract motion objects within frames to generate the context-based initial codebook. Furthermore, Differential Pulse Code Modulation (DPCM) is utilized for lossless compression of significant wavelet coefficients; whereas low energy coefficients are lossy compressed using Learning Vector Quantization (LVQ) neural networks. Finally, Run Length Encoding (RLE) is engaged to encode the quantized coefficients to achieve a higher compression ratio. Experiments have proven the system’s ability to achieve higher compression ratio with acceptable efficiency measured by PSNR.

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 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.

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.

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.

Novel reversible data hiding scheme for Two-stage VQ compressed images based on search-order coding

In this paper, a novel reversible data hiding scheme for Two-stage VQ (Vector quantization) compressed images based on SOC (Search-order coding) scheme is proposed. Two-stage VQ improves VQ by obtaining better reconstructed image and generating indices with higher correlation. The difference image, as the input of Two-stage VQ, is produced by employing the first codeword in state codebook and the current image block. The main idea of SOC method is exploiting the correlation of indices to derive better compression performance. The combination of SOC and data hiding can achieve both high compression rate and high embedding capacity. Since Two-stage VQ can achieve indices with higher correlation, this advantage is applied to enhance the data hiding performance. Moreover, the cover image can be reconstructed by the receiver without using any side information. To show the superiority of our proposed scheme, several state-of-the-art schemes designed for compression domain are cited for comparison.

A novel reversible data hiding scheme by introducing current state codebook and prediction strategy for joint neighboring coding

Reversible data hiding (RDH) in compression domain is an important research issue in the security of digital multimedia. Obtaining a high embedding rate and a low compression rate are the main goals of compression domain RDH. This paper proposes a novel RDH scheme to improve joint neighboring coding (JNC) scheme. In embedding process, the first index SC 1st in current state codebook (SC) and median edge detector (MED) prediction P med are exploited. These two parameters are employed to replace the right-up and left-up neighboring SMVQ indices, which have lower correlation with the current index. As a result, a more concentrated distribution of difference “d” is obtained. Difference “d” is computed by the difference between the current SMVQ index and its left, upper neighboring indices, P med and SC 1st after embedding secret bits. The experimental results show that our work achieves the average compression rate of 0.45/0.51/0.57 bpp and the average embedding efficiency of 0.28/0.36/0.43 after embedding 2/3/4 bits secret data into each SMVQ index. As demonstrated in the comparative results, it can be observed that the proposed scheme outperforms the other previous works.

Rapid Generation of the State Codebook in Side Match Vector Quantization

Rapid Generation of the State Codebook in Side Match Vector Quantization

A Scalable Approach for Content-Based Image Retrieval in Peer-to-Peer Networks

Peer-to-peer networking offers a scalable solution for sharing multimedia data across the network. With a large amount of visual data distributed among different nodes, it is an important but challenging issue to perform content-based retrieval in peer-to-peer networks. While most of the existing methods focus on indexing high dimensional visual features and have limitations of scalability, in this paper we propose a scalable approach for content-based image retrieval in peer-to-peer networks by employing the bag-of-visual-words model. Compared with centralized environments, the key challenge is to efficiently obtain a global codebook, as images are distributed across the whole peer-to-peer network. In addition, a peer-to-peer network often evolves dynamically, which makes a static codebook less effective for retrieval tasks. Therefore, we propose a dynamic codebook updating method by optimizing the mutual information between the resultant codebook and relevance information, and the workload balance among nodes that manage different codewords. In order to further improve retrieval performance and reduce network cost, indexing pruning techniques are developed. Our comprehensive experimental results indicate that the proposed approach is scalable in evolving and distributed peer-to-peer networks, while achieving improved retrieval accuracy.

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.

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.

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.

Lossless VQ Indices Compression Based on the High Correlation of Adjacent Image Blocks

Traditional vector quantization (VQ) schemes encode image blocks as VQ indices, in which there is significant similarity between the image block and the codeword of the VQ index. Thus, the method can compress an image and maintain good image quality. This paper proposes a novel lossless VQ indices compression algorithm to further compress the VQ index table. Our scheme exploits the high correlation of adjacent image blocks to search for the same VQ index with the current encoding index from the neighboring indices. To increase compression efficiency, codewords in the codebook are sorted according to the degree of similarity of adjacent VQ indices to generate a state codebook to find the same index with the current encoding index. Note that the repetition indices both on the search path and in the state codebooks are excluded to increase the possibility for matching the current encoding index. Experimental results illustrated the superiority of our scheme over other compression schemes in the index domain.

Tangent Lévy market models

In this paper, we introduce a new class of models for the time evolution of the prices of call options of all strikes and maturities. We capture the information contained in the option prices in the density of some time-inhomogeneous Levy measure (an alternative to the implied volatility surface), and we set this static code-book in motion by means of stochastic dynamics of Ito’s type in a function space, creating what we call a tangent Levy model. We then provide the consistency conditions, namely, we show that the call prices produced by a given dynamic code-book (dynamic Levy density) coincide with the conditional expectations of the respective payoffs if and only if certain restrictions on the dynamics of the code-book are satisfied (including a drift condition a la HJM). We then provide an existence result, which allows us to construct a large class of tangent Levy models, and describe a specific example for the sake of illustration.

Reversible steganography based on side match and hit pattern for VQ-compressed images

Data hiding is an important technique in multimedia security. Multimedia data hiding techniques enable message senders to disguise secret data by embedding them into cover media. Thus, delivering secret messages is as easy as sending the cover media. Recently, many researchers have studied reversible data hiding for images. Those methods can reconstruct the original cover image and extract the embedded secret data from a stego-image. This study proposes a novel reversible steganographic method of embedding secret data into a vector quantization (VQ) compressed image by applying the concept of side match. The proposed method uses extra information, namely the hit pattern, to achieve reversibility. Moreover, its small hit pattern enables the embedding of the entire hit pattern along with the secret data in most cases. To optimize visual quality of the output stego-image, the method applies the concept of partitioned codebooks (state codebooks). The partition operation on the codebook uses a look-up table to minimize embedding and extraction time. We also propose the use of diagonal seed blocks to embed the entire hit pattern into the cover image without producing any extra control messages. Compared to the Chang and Lin method, the experimental results show that the proposed method has higher capacity, better visual quality, and shorter execution time.

Lossless Compression Schemes of Vector Quantization Indices Using State Codebook

In a memoryless vector quantization system, each image block is independently encoded as a corresponding index and then an index table will be generated. In this paper, we propose two novel schemes to compress the index table without introducing extra encoding distortion. Both our schemes exploit the characteristic that the blocks of images are highly correlated to find the same or similar ndex from the neighboring indices in the compression process. To increase the compression, the principal component analysis (PCA) technique is also employed to sort the codewords in the codebook for minimizing the difference of neighboring indices. In addition, our second scheme excludes the repetitive indices from the search path to further decrease the bit rate. Simulation results show that our schemes are superior to SOC and traditional memoryless VQ on the compression rate.

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.