Reduced complexity two stage vector quantization

We address the issue of complexity for vector quantization (VQ) of wide-band speech LSF (line spectrum frequency) parameters. The recently proposed switched split VQ (SSVQ) method provides better rate–distortion (R/D) performance than the traditional split VQ (SVQ) method, even at the requirement of lower computational complexity, but at the expense of much higher memory. We develop the two stage SVQ (TsSVQ) method, by which we gain both the memory and computational advantages and still retain good R/D performance. The proposed TsSVQ method uses a full dimensional quantizer in its first stage for exploiting all the higher dimensional coding advantages and then, uses an SVQ method for quantizing the residual vector in the second stage so as to reduce the complexity. We also develop a transform domain residual coding method in this two stage architecture such that it further reduces the computational complexity. To design an effective residual codebook in the second stage, variance normalization of Voronoi regions is carried out which leads to the design of two new methods, referred to as normalized two stage SVQ (NTsSVQ) and normalized two stage transform domain SVQ (NTsTrSVQ). These two new methods have complimentary strengths and hence, they are combined in a switched VQ mode which leads to the further improvement in R/D performance, but retaining the low complexity requirement. We evaluate the performances of new methods for wide-band speech LSF parameter quantization and show their advantages over established SVQ and SSVQ methods.     

Learning vector quantization with training data selection

In this paper, we propose a method that selects a subset of the training data points to update LVQ prototypes. The main goal is to conduct the prototypes to converge at a more convenient location, diminishing misclassification errors. The method selects an update set composed by a subset of points considered to be at the risk of being captured by another class prototype. We associate the proposed methodology to a weighted norm, instead of the Euclidean, in order to establish different levels of relevance for the input attributes. The technique was implemented on a controlled experiment and on Web available data sets.     

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.     

基于改进量化约束集的块效应消除算法

低比特率时,基于块离散余弦变换（BDCT）的图像压缩方法常常会引起块效应。量化约束集（QCS）作为关于编码模型的重要先验信息在块效应消除方法中被广泛地采用。利用量化误差的统计特性,提出了一种改进的量化约束集（MQCS）。实验结果表明当在现有的基于凸集投影（POCS）的去块算法中以新的约束集代替传统量化约束集,峰值信噪比（PSNR）和主观图像质量有不同程度的提高,性能与窄量化约束集（NQCS）相当。     

Low bit-rate voice compression based on frequency domain interpolative techniques

This paper presents an approach, referred to as frequency domain interpolation (FDI), for achieving high-quality speech at low bit-rates (4 kb/s and below) within reasonable complexity and delay. FDI methods, like the prototype waveform interpolation (PWI) methods, derive a prototype waveform (PW) at regular intervals of time. But, unlike PWI, there is no separation into a slowly evolving waveform (SEW) and a rapidly evolving waveform (REW) component. Instead, the PW is encoded after gain normalization in magnitude-phase form. The magnitude is modeled as a sum of mean and deviation values in multiple frequency bands and this model is quantized using switched backward adaptive VQ techniques. The phase information is represented as a composite vector of PW correlations in multiple frequency bands and an overall voicing measure. This information is quantized using a VQ at the encoder. At the decoder, a phase model is employed that uses the received phase (and magnitude) information to reproduce PWs with the correct periodicity and evolutionary characteristics. Speech is synthesized by interpolating the reconstructed PWs after gain adjustment and filtering it using the short-term predictor and a postfilter. The design of a 4-kb/s and a 2.4-kb/s FDI codec are presented in this paper and their performance is characterized in terms of delay, complexity, and subjective voice quality. The results confirm that FDI techniques have the potential for delivering high-quality speech at low bit-rates in a cost-effective manner.     

Low bit-rate speech coding based on multicomponent AFM signal model

In this paper, we propose a novel multicomponent amplitude and frequency modulated (AFM) signal model for parametric representation of speech phonemes. An efficient technique is developed for parameter estimation of the proposed model. The Fourier–Bessel series expansion is used to separate a multicomponent speech signal into a set of individual components. The discrete energy separation algorithm is used to extract the amplitude envelope (AE) and the instantaneous frequency (IF) of each component of the speech signal. Then, the parameter estimation of the proposed AFM signal model is carried out by analysing the AE and IF parts of the signal component. The developed model is found to be suitable for representation of an entire speech phoneme (voiced or unvoiced) irrespective of its time duration, and the model is shown to be applicable for low bit-rate speech coding. The symmetric Itakura–Saito and the root-mean-square log-spectral distance measures are used for comparison of the original and reconstructed speech signals.     

基于向量量化的图像超分辨率方法

受分形编码思想启发,提出了一种新的基于向量量化的图像超分辨率方法。该方法使用学习算法来获取单幅输入图像中的高频信息和低频信息之间的对应关系,并利用此关系对输入图像的一个倍频程的空间频率内添加图像细节以获得高分辨率图像。该方法克服了传统插值方法中因过度平滑导致图像模糊和纹理保持较差的缺点,能够重现出传统插值方法不能复原出的一些高频图像细节。实验结果显示该算法在客观和主观上都比传统插值方法有更好的评价。     

Image retrieval based on quadtree classified vector quantization

Multimedia Tools and Applications - In this paper, a color image retrieval scheme based on quadtree classified vector quantization (QCVQ) is proposed. This scheme not only captures intra-block...     

Image retrieval based on index compressed vector quantization

Increased amount of visual data in several applications necessitates content-based image retrieval. Since most of visual data is stored in compressed form, it is crucial to develop indexing techniques for searching images based on their content in compressed form. Therefore, it is desirable to explore image compression techniques with capability of describing image content in compressed form. Vector Quantization (VQ) is a compression scheme that exploits intra-block correlation and image correlation reflects image content, hence VQ is a suitable compression technique for compressed domain image retrieval.This paper introduces a novel indexing scheme for compressed domain image databases based on indices generated from IC-VQ. The proposed scheme extracts image features based on relationship between indices of IC-VQ compressed images. This relationship detects contiguous regions of compressed image based on inter- and intra-block correlation. Experimental results show effectiveness superiority of the new scheme compared to VQ and color-based schemes.     

半监督矢量量化的汉语方言辨识

提出了一种新的方言码本模型辨识系统。该方法利用半监督的思想对方言语音数据进行矢量量化,形成具有监督信息的码本模型。有效解决了在汉语方言辨识中码本精度不高的问题,系统的识别率有了很大提高。实验结果表明带有监督信息的码本量化方法明显优于传统LBG矢量量化方法,对于汉语三种方言,辨识率可达94.23%,比传统码本辨识系统提高了近13%的正确辨识率。     

High-capacity image hiding scheme based on vector quantization

A novel grayscale image hiding scheme that is capable of hiding multiple secret images into a host image of the same size is proposed in this paper. The secret images to be hidden are first compressed by vector quantization with additional index compression process. Then, the compressed secret images are encrypted and embedded into the least-significant bits of the host pixels. To provide good image quality of the stego-image, the modulus function and the image property are employed to hide the secret bits into the host pixels and determine the number of hidden bits in each host pixel, respectively. According to the results, the proposed scheme provides a higher hiding capacity and a higher degree of security than that of the virtual image cryptosystem.     

Image indexing and retrieval based on vector quantization

To effectively utilize information stored in a digital image library, effective image indexing and retrieval techniques are essential. This paper proposes an image indexing and retrieval technique based on the compressed image data using vector quantization (VQ). By harnessing the characteristics of VQ, the proposed technique is able to capture the spatial relationships of pixels when indexing the image. Experimental results illustrate the robustness of the proposed technique and also show that its retrieval performance is higher compared with existing color-based techniques.     

一种基于偶数格的格型矢量量化方法

提出一种适用于音频信号的格型矢量量化方法,该方法利用偶数格的特点实现快速的索引分配以及基础码本的压缩存储;将基础码本与球型扩展方法相结合,减小感知重要频带的量化误差。经实验验证,此方法与ITU-T的G729.EV标准中矢量量化效果相当。     

Constrained-storage multistage vector quantization based on genetic algorithms

Multistage vector quantization (MSVQ) and their variants have been recently proposed. Before MSVQ is designed, the user must artificially determine the number of codewords in each VQ stage. However, the users usually have no idea regarding the number of codewords in each VQ stage, and thus doubt whether the resulting MSVQ is optimal. This paper proposes the genetic design (GD) algorithm to design the MSVQ. The GD algorithm can automatically find the number of codewords to optimize each VQ stage according to the rate–distortion performance. Thus, the MSVQ based on the GD algorithm, namely MSVQ(GD), is proposed here. Furthermore, using a sharing codebook (SC) can further reduce the storage size of MSVQ. Combining numerous similar codewords in the VQ stages of MSVQ produces the codewords of the sharing codebook. This paper proposes the genetic merge (GM) algorithm to design the SC of MSVQ. Therefore, the constrained-storage MSVQ using a SC, namely CSMSVQ, is proposed and outperforms other MSVQs in the experiments presented here.     

一种基于索引约束矢量量化的脆弱音频水印算法*

与传统矢量量化不同,索引约束矢量量化在量化过程中通过约束码字索引二进制形式中某一位的值来限定码字的搜索范围。本文利用其特殊的码字搜索方法提出了一种在音频信号中嵌入水印的方法。将原始音频信号分段,每段进行DCT变换并提取若干中频系数构成矢量。水印嵌入时根据水印比特信息和预先设定的索引约束位的值找到匹配码字修改各段DCT中频系数。水印提取时利用传统矢量量化方法得到各量化索引值后,提取出各索引值中与嵌入端相同位的比特值即为水印信息。该方法在量化过程中嵌入水印信息,有很好的实时性。实验结果表明,利用该方法嵌入的水印为一种脆弱水印,可用于认证。     

Efficient on-line signature recognition based on multi-section vector quantization

This paper proposes a multi-section vector quantization approach for on-line signature recognition. We have used a database of 330 users which includes 25 skilled forgeries performed by 5 different impostors. This database is larger than those typically used in the literature. Nevertheless, we also provide results from the SVC database. Our proposed system obtains similar results as the state-of-the-art online signature recognition algorithm, Dynamic Time Warping, with a reduced computational requirement, around 47 times lower. In addition, our system improves the database storage requirements due to vector compression, and is more privacy-friendly because it is not possible to recover the original signature using the codebooks. Experimental results reveal that our proposed multi-section vector quantization achieves a 98% identification rate, minimum Detection Cost Function value equal to 2.29% for random forgeries and 7.75% for skilled forgeries.     

A robust associative watermarking technique based on vector quantization

In this paper, the concepts of vector quantization (VQ) and association rules in data mining are employed to propose a robust watermarking technique. Unlike ordinary or traditional watermarking techniques, our approach hides association rules of the watermark, instead of the whole watermark; in other words, the embedded information is the association rules of the watermark. First, VQ encoding is performed on the original image and watermark to generate the index tables, and from which association rules are further mined. Subsequently, by embedding the association rules of the watermark into the association rules of the original image, the purpose for watermarking is accomplished. Finally, VQ decoding technique is applied to reconstruct the watermarked image from the watermarked index table. Experimental results show that our proposed method achieves effective resistance against several image processings such as blurring, sharpening, adding in Gaussian noise, cropping, and JPEG lossy compression. Moreover, the embedding capacity is also significantly increased, so any a complex watermark image is still acceptable in this method.     

基于矢量量化的弦乐颤音自动检测

自动音乐标注是音频信息检索的基础,并可广泛应用于辅助音乐教学、辅助音乐创作等许多音乐相关领域。然而,在弦乐器演奏的音乐中存在着大量用于修饰或表现乐曲情感、风格的颤音。在对这类乐器的自动音乐标注中,如果不进行颤音检测而直接进行标注容易出现错误。对基于矢量量化的弦乐颤音识别方法进行了研究,提出了一种用于对整段音乐进行颤音检测的方法。实验证明这种方法是精确有效的。     

矢量量化用于颜色图像检索的改进方法

为了消除基于颜色的图像检索在颜色空间分布描述方向的不足,提出一种新的基于颜色的检索方法。引入颜色转移矩阵描述颜色的空间分布,再结合颜色直方图和颜色转移矩阵进行复合图像检索。同时通过矢量量化方法量化图像颜色得到颜色直方图和颜色转移矩阵,实现了在压缩领域进行图像检索,减少了额外计算负担。实验表明,该方法能有效提高检索效率和精度。     

基于支持向量数据描述算法的SVM多分类新方法*

提出一种基于支持向量数据描述算法(SVDD)的多分类方法(S-MSVM).受SVDD的启发,该方法对每类样本建立一个超球来界定,但训练好的超球在所有情况下都是相交的.选择相交区域的样本单独建立超球,重复该步骤,直到相交区域消失或相交区域内没有样本点.给出了该方法的时间复杂度分析,并通过实验验证了该方法具有相对较好的训练精度.