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.     

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.     

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 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.     

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.     

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.     

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.     

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.     

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.     

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。     

A multipurpose audio aggregation watermarking based on multistage vector quantization

Watermarking technology can achieve multipurpose such as copyright protection, copy protection, and integrity authentication. We propose a novel watermarking approach which involves robust watermark and fragile watermark in a two-stage quantization technique. Traditional watermarking algorithms mostly have poor performance in imperceptibility as the codeword selected from the modified codebook is not optimal. In our method, we select the codeword in the original codebook to ensure optimality. Furthermore, we use Huffman encoding to pick up property which is sensitive to many attacks in the entire aggregation. For copyright protection, the proposed scheme can resist attacks such as lossy compression, noise addition, and normalization. As for content authentication, the proposed scheme is sensitive to various attacks provided by Stirmark Benchmark for Audio. Experimental results show that the proposed method can be used respectively for protecting the copyright and authenticating the integrity of the audio aggregation.     

基于支持向量机的多因素话务量预测研究

提高移动通信话务量的预测精度对提高网络性能、增进用户体验具有重要意义。由于多种因素会影响到移动通信话务量的准确预测,故选择多因素灰色话务量预测模型来预测话务量。先对数据进行预处理,用关联分析法找到影响话务量预测的主要因素。但此模型对波动较大的数据预测精度较低,用支持向量机的模型来改善预测结果,选取拥有较强的敛散性和全局寻优能力的复高斯小波核函数优化向量机。从仿真结果可以看出该模型有更好的收敛作用和较为理想的预测效果。     

基于分裂预测式矢量量化的研究

为降低误差积累对线谱频率量化的影响,防止因此而导致语音质量下降,本文提出了基于分裂预测方法的新型混合式矢量量化结构.实验结果表明,该方法不仅比G.29的语音编解码算法明显降低了累积误差,平均谱失真在丢帧严重的情况下降低了达36%,而且提高了量化的透明度.     

Image coding algorithm based on Hadamard transform and simple vector quantization

Transform coding is commonly used in image processing algorithms to provide high compression ratios, often at the expense of processing time and simplicity of the system. We have recently proposed a pixel value prediction scheme in order to exploit adjacent pixel correlation, providing a low-complexity model for image coding. However, the proposed model was unable to reach high compression ratios retaining high quality of reconstructed image at the same time. In this paper we propose a new segmentation algorithm which further utilizes adjacent pixel correlation, provides higher compression ratios and it is based on application of Hadamard transform coding. Additional compression is provided by using vector quantization for a low number of quantization levels and by simplifying generalized Lloyd’s algorithm where the special attention is paid to determination of optimal partitions for vector quantization, making a fixed quantizer. The proposed method is quite simple and experimental results show that it ensures better or similar rate-distortion ratio for very low bit-rates, comparing to the other similar methods that are based on wavelet or curvelet transform coding and support or core vector machine application. Furthermore, the proposed method requires very low processing time since the proposed quantizers are fixed, much less than the required time for the aforementioned methods that we compare with as well as much less than the time required for fractal image coding. In the end, the appropriate discussion is provided comparing the results with a scheme based on linear prediction and dual-mode quantization.

     

粒子群优化算法在图像矢量量化码书设计中的应用

鉴于经典的LBG码书设计算法易陷入局部最优解,首次采用粒子群优化算法来设计图像矢量量化的最优码书,并提出了粒子群矢量量化（PSO-VQ）算法和粒子一致性操作（PCO）。在PSO-VQ算法中,每个粒子表示一个码书,以粒子群进化的方式对初始码书进行迭代而获得最优码书,PCO操作对各初始码书中的码矢量按其灰度均值排序,使不同码书的内部结构基于码矢量灰度均值达到基本一致,确保了结果向全局最优解收敛。实验证明,PSO-VQ算法在解码图像的PSNR值和主观效果上都优于LBG算法,同时拓展了粒子群优化算法的应用领域。     

FPGA implementation of full-search vector quantization based on partial distance search

This paper presents a novel algorithm for field programmable gate array (FPGA) realization of vector quantizer (VQ) encoders using partial distance search (PDS). In most applications, the PDS is adopted as a software approach for attaining moderate codeword search acceleration. In this paper, a novel PDS algorithm well suited for hardware realization is proposed. The algorithm employs subspace search, bitplane reduction, and multiple-coefficient accumulation techniques for the effective reduction of the area complexity and computation latency. Concurrent encoding of different input vectors for further computation acceleration is also allowed by the employment of multiple-module PDS. The proposed implementation has been embedded in a softcore CPU for physical performance measurement. Experimental results show that the implementation provides a cost-effective solution to the FPGA realization of VQ encoding systems where both high throughput and high fidelity are desired.     

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.