Predictive Vector Quantization Research Articles

KG2Vec: A node2vec-based vectorization model for knowledge graph.

Since the word2vec model was proposed, many researchers have vectorized the data in the research field based on it. In the field of social network, the Node2Vec model improved on the basis of word2vec can vectorize nodes and edges in social networks, so as to carry out relevant research on social networks, such as link prediction, and community division. However, social network is a network with homogeneous structure. When dealing with heterogeneous networks such as knowledge graph, Node2Vec will lead to inaccurate prediction and unreasonable vector quantization data. Specifically, in the Node2Vec model, the walk strategy for homogeneous networks is not suitable for heterogeneous networks, because the latter has distinguishing features for nodes and edges. In this paper, a Heterogeneous Network vector representation method is proposed based on random walks and Node2Vec, called KG2vec (Heterogeneous Network to Vector) that solves problems related to the inadequate consideration of the full-text semantics and the contextual relations that are encountered by the traditional vector representation of the knowledge graph. First, the knowledge graph is reconstructed and a new random walk strategy is applied. Then, two training models and optimizing strategies are proposed, so that the contextual environment between entities and relations is obtained, semantically providing a full vector representation of the Heterogeneous Network. The experimental results show that the KG2VEC model solves the problem of insufficient context consideration and unsatisfactory results of one-to-many relationship in the vectorization process of the traditional knowledge graph. Our experiments show that KG2vec achieves better performance with higher accuracy than traditional methods.

Fusion of dynamic predictive block adaptive quantization and vector quantization for staggered SAR data compression

ABSTRACT Staggered synthetic aperture radar (SAR) is an advanced concept for high-resolution wide-swath (HRWS) imaging, which overcomes the problem of blind ranges by continuously varying the pulse repetition frequency (PRF). For better performance of information recovery in blind ranges, data are typically highly oversampled in azimuth for staggered SAR system. Thus a huge data volume onboard is required and the demands for internal data storage and downlink capacity are increasing. In this paper, we investigate a novel method, named Dynamic Predictive Block Adaptive Vector Quantization (DP-BAVQ), to reduce the volume of downlinked data for staggered SAR. It firstly compresses the difference of the raw data and their predictions with the dynamic predictive block adaptive quantization (DP-BAQ). Then a secondary compression is performed with vector quantization (VQ). The simulation results and the experiments with real data show that the proposed method achieves an improvement in the signal-to-quantization noise ratio (SQNR) compared with DP-BAQ. Moreover, DP-BAVQ at 2 bits/sample provides a higher SQNR compared with standard block adaptive quantization (BAQ) at 3 bits/sample. Thus the proposed method allows for a significant reduction of data volume.

Humpback whale song analysis based on automatic classification performance

Song is a prevalent behavior in humpback whale populations, even in regions that are considered to be foraging habitat such as the Monterey Bay National Marine Sanctuary in the Northeast Pacific, where song is detected nine months out of the year. In this work, we explore various machine learning methods to classify song units, as a basis for studying song structure and its changes. We report on a number of analyses and classification exercises based on linear predictive coding, vector quantization, and machine learning classifiers including Naive Bayes, first-order Markov chain, and Hidden Markov modeling. As a baseline for comparison purposes, the distortion measure used to create the codebooks for vector quantization is itself also used as a means for classification. With classification accuracy ranging from 65% to 83% across the selected methods on a 4.5 h recording involving 5000 unit occurrences over 20 different unit types, we evaluate the effect of several signal processing, clustering, and learning parameters on classification performance with the goal of laying a foundation that can be used to characterize song vocalization at not only the unit level but also below (subunit) and above (phrase, theme, song).

Single and multiple frame coding of LSF parameters using deep neural network and pyramid vector quantizer

In linear predictive speech coders, the linear predictive coding (LPC) parameters are usually transformed into the line spectral frequency (LSF) representation for quantization. In this paper, the single and multiple frame coding of LSF parameters using deep neural network (DNN) and pyramid vector quanrizer (PVQ) are proposed. In the single-frame scheme, a non-linear DNN predictor which demonstrates much better prediction performance than the autoregressive (AR) model is applied to exploit the inter-frame dependency of LSF parameters. The prediction residual signal has Laplacian distribution and can be efficiently quantized by the PVQ. The performance evaluation using spectral distortion shows that the proposed DNN predictive PVQ outperforms the AR predictive split vector quantization (SVQ). In the multiple-frame scheme, a deep autoencoder possessing linear coder-layer units with Gaussian noise is used to compress and de-correlate multiple LSF frames. The deep autoencoder shows a high degree of modelling flexibility for multiple LSF frames. To quantize the coder-layer vector effectively, a PVQ is considered. The experimental results show that the proposed multi-frame scheme with determined optimal coder-layer dimension outperforms the discrete cosine model (DCM)-based approach in terms of spectral distortion performance and robustness across different speech segments.

The linear prediction vector quantization for hyperspectral image compression

In this paper, a hyperspectral image compression method is proposed. It is based on spectral clustering, linear prediction and the vector quantization (VQ). Since the hyperspectral image has stronger spectral correlation than spatial correlation, the spectral clustering and model of linear prediction are introduced to reduce the spectral correlation. In the proposed method, spectral clustering algorithm of K-means is employed, and the centroids of clustered results are used as reference bands, then the reference bands are employed in the model of linear prediction to compute the prediction error, finally the prediction error is encoded by VQ. The experiments results using AVIRIS images are compared to IVQ and AR + SubPCA+JPEG2000 algorithm, the results show that our proposed algorithm outperforms other algorithms.

Lossy image compression based on prediction error and vector quantisation

Lossy image compression has been gaining importance in recent years due to the enormous increase in the volume of image data employed for Internet and other applications. In a lossy compression, it is essential to ensure that the compression process does not affect the quality of the image adversely. The performance of a lossy compression algorithm is evaluated based on two conflicting parameters, namely, compression ratio and image quality which is usually measured by PSNR values. In this paper, a new lossy compression method denoted as PE-VQ method is proposed which employs prediction error and vector quantization (VQ) concepts. An optimum codebook is generated by using a combination of two algorithms, namely, artificial bee colony and genetic algorithms. The performance of the proposed PE-VQ method is evaluated in terms of compression ratio (CR) and PSNR values using three different types of databases, namely, CLEF med 2009, Corel 1 k and standard images (Lena, Barbara etc.). Experiments are conducted for different codebook sizes and for different CR values. The results show that for a given CR, the proposed PE-VQ technique yields higher PSNR value compared to the existing algorithms. It is also shown that higher PSNR values can be obtained by applying VQ on prediction errors rather than on the original image pixels.

A 600bps Vocoder Algorithm Based on MELP

In order to meet the bandwidth requirements of low-bit-rate speech communication, there is an improvement of standard Mixed Excitation Linear Prediction (MELP) encoder and a 600bps MELP vocoder algorithm with higher quality is proposed. The algorithm forms a super frame by using 5 sub-frames. Joint quantization was carried out to remove redundancy information between intra-frames and inter-frames. We use the method of predictive split vector quantization to quantize the Line Spectral Frequency (LSF) parameters and the other parameters are jointly quantized. Compared to standard 2.4kbps MELP vocoder, the Mean Opinion Score(MOS) of the synthetic speech which is synthesized by the vocoder using the proposed algorithm is 2.54, and the synthetic speech quality is good.

Predictive Vector Quantization for Multicell Cooperation with Delayed Limited Feedback

Base station cooperation can use knowledge of the users' channel state information (CSI) at the transmitters to manage co-channel interference. A reasonable way to provide CSI to the base stations is through a finite rate limited feedback channel. Existing multicell limited feedback techniques require a large amount of feedback, which incurs an overhead penalty on the uplink. In this paper, a new feedback approach based on predictive vector quantization (PVQ) is proposed to reduce feedback requirements in multicell systems and provide high resolution CSI at base stations by exploiting temporal correlation in the channels. Transmitter and receiver structures are proposed to implement predictive limited feedback accounting for delay, for signals on the Grassmann manifold. Simulations show that the proposed PVQ framework yields higher sum-rates than memoryless quantization approaches for multicell limited feedback, in a cooperative system using intercell interference nulling.

A Novel Predictive Quantization Feedback Scheme for Zero-Forcing Beamforming System

In multiuser MIMO (MU-MIMO) system, Channel State Information (CSI) is crucial at the Base Station (BS) to achieve maximum multiplexing gains on the broadcast channel. Limited feedback is a flexible approach to provide partial CSI to the BS. Predictive Vector Quantization (PVQ) which exploits the channel’s temporal correlation can compress the feedback overhead and significantly reduce the quantization error compared with conventional Vector Quantization (VQ). In this paper, we propose a simplified predictive quantization algorithm in which, the magnitude and direction of the prediction error are quantized separately. Based on the feedback indices, the BS reconstructs the prediction error and corrects the prediction value. We also apply the algorithm to Zero-forcing (ZF) MU-MIMO system, and derive a sum rate lower bound relevant to the effect of the quantization errors.

Multi-frame AR model을 이용한 LPC 계수 양자화

음성코딩 시 성도는 Linear Predictive Coding (LPC) 계수를 이용해서 모델링 한다. 일반적으로 LPC 계수는 양자화와 선형보간 관점에서 유리한 Line Spectral Frequency (LSF) 파라미터로 변경하여 사용한다. 10차 이상의 다차원 LSF 데이터를 벡터 양자화를 이용하여 직접 코딩하게 되면 벡터 내 상관관계 (intra-frame correlation)를 모두 이용할 수 있으므로 rate-distortion 관점에서는 높은 효율을 기대할 수 있다. 하지만, 계산량과 메모리 요구량이 높아져서 실제 코딩 시스템에서는 사용할 수 없게 되므로, 차원을 나누어 압축하는 Split Vector Quantization (SVQ)이 이용된다. 또한, LSF 데이터는 과거 벡터와의 벡터 간 상관관계 (inter-frame correlation)가 높으므로, 이를 이용한 Predictive Split Vector Quantization (PSVQ)이 사용되고 있다. PSVQ는 SVQ 보다 높은 rate-distortion 성능을 보인다. 본 논문에서는 음성 저장 장치를 위한 최적의 PSVQ를 구현하기 위해서 다수의 과거 프레임 정보와의 벡터 간상관관계 (inter-frame correlation)를 고려한 Multi-Frame AR-model 기반 SVQ (MF-AR-SVQ)를 제안하였다. 기존 PSVQ와 비교해 보았을 때, MF-AR-SVQ는 계산량과 메모리 요구량의 큰 증가 없이, 평균 spectral distortion 관점에서 약 1비트의 성능 향상을 보였다. For speech coding, a vocal tract is modeled using Linear Predictive Coding (LPC) coefficients. The LPC coefficients are typically transformed to Line Spectral Frequency (LSF) parameters which are advantageous for linear interpolation and quantization. If multidimensional LSF data are quantized directly using Vector-Quantization (VQ), high rate-distortion performance can be obtained by fully utilizing intra-frame correlation. In practice, since this direct VQ system cannot be used due to high computational complexity and memory requirement, Split VQ (SVQ) is used where a multidimensional vector is split into multilple sub-vectors for quantization. The LSF parameters also have high inter-frame correlation, and thus Predictive SVQ (PSVQ) is utilized. PSVQ provides better rate-distortion performance than SVQ. In this paper, to implement the optimal predictors in PSVQ for voice storage devices, we propose Multi-Frame AR-model based SVQ (MF-AR-SVQ) that considers the inter-frame correlations with multiple previous frames. Compared with conventional PSVQ, the proposed MF-AR-SVQ provides 1 bit gain in terms of spectral distortion without significant increase in complexity and memory requirement.

Low Bit Rate Speech Coding Using Lattice Vector Quantization and Time-Scale Modification

This paper presents a low bit rate speech coder based on predictive lattice vector quantization (PLVQ) and time-scale modification (TSM). The coding model of proposed vocoder is built on the MELP, in which bit rate reduction is achieved by taking advantage of PLVQ and TSM techniques. PLVQ is used to encode the speech line spectrum pair (LSP) parameters, which has the advantage of lower implementation complexity than multi-stage vector quantization (MSVQ), moreover, it does not require memory for codebook storage. With our speech data base, PLVQ can save up to 4 bits/frame compared to unstructured codebook MSVQ. TSM can change the speed of speech signal with its perceptual characteristics remained. Through appending TSM as previous and post process, speech coding at bit rate about 1.1 kbps could be easily achieved without modifying the vocoder structure.

3D Mesh Compression by Generalized Parallelogram Predictive Vector Quantization

3D Mesh Compression by Generalized Parallelogram Predictive Vector Quantization

JPEG Coding System Based on Mean Value Predictive Vector Quantization

Image compression is a basic need for the progressive transmission or storage of image data via any media. For the effective processing of image data the coding standards were developed. The most commonly used standard in current scenario is the JPEG and JPEG-2000 coding. These standards have become the industry standards and are progressively been in use for almost all image processing applications. In the process of image coding various sub-processing operations occur which process the image blocks by transformation, quantization and encoding before transmitting or storing. In the process, quantization is observed to be a time consuming and is needed to be processed in high precision so as to achieve less processing error and also in reasonable time. The process of quantization has shown greater efficiency in vector quantization than earlier methods. Vector quantization is observed to be an efficient block based lossy compression technique. Due to simpler coding process and high compression ratio VQ are widely used. Predictive vector Quantization (PVQ) is commonly used memory based vector Quantization method. The main drawback with PVQ is the high computational complexity which limits PVQ to use in real time applications. Shorter coding time can be archived by reducing the number of code words or instructions, but the reconstructed image quality will degraded in the case of fewer code words. Hence, it is necessary to propose fast coding algorithms for PVQ. In this paper a modified version of the PVQ approach has been suggested in order to process faster, which ensue to be a compatible mechanism for real time image coding in JPEG based coding system. Keywords: Compression, quantization, PVQ encoder, mean-square-error, Discrete Cosine Transform.

Predictive Vector Quantization for Images Based on Essential Curved Surface Units

Predictive Vector Quantization for Images Based on Essential Curved Surface Units

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.

Block Constrained Trellis Coded Vector Quantization of LSF Parameters for Wideband Speech Codecs

In this paper, block constrained trellis coded vector quantization (BC-TCVQ) is presented for quantizing the line spectrum frequency parameters of the wideband speech codec. Both a predictive structure and a safety-net concept are combined into BC-TCVQ to develop the predictive BC-TCVQ. The performance of this quantization is compared with that of the linear predictive coding vector quantizer used in the AMR-WB codec, demonstrating reductions in spectral distortion.

A Statistical Approach to Error Compensation in Spectral Quantization

In this paper, we propose a statistical approach to improve the performance of spectral quantization of speech coders. The proposed techniques compensate for the distortion in a decoded line spectrum pair (LSP) vector based on a statistical mapping function between a decoded LSP vector and its corresponding original LSP vector. We first develop two codebook-based probabilistic matching (CBPM) methods by investigating the distribution of LSP vectors. In addition, we propose an iterative procedure for the two CBPMs. Next, the proposed techniques are applied to the predictive vector quantizer (PVQ) used for the IS-641 speech coder. The experimental results show that the proposed techniques reduce average spectral distortion by around 0.064 dB and the percentage of outliers compared with the PVQ without any compensation, resulting in transparent quality of spectral quantization. Finally, the comparison of speech quality using the perceptual evaluation of speech quality (PESQ) measure is performed and it is shown that the IS-641 speech coder employing the proposed techniques has better decoded speech quality than the standard IS-641 speech coder.

Memory-Based Vector Quantization of LSF Parameters by a Power Series Approximation

In this paper, memory-based quantization is studied in detail. We propose a new framework, power series quantization (PSQ), for memory-based quantization. With linear spectral frequency (LSF) quantization as the application, several common memory-based quantization methods (FSVQ, predictive VQ, VPQ, safety-net, etc.) are analyzed and compared with the proposed method, and it is shown that the proposed method performs better than all other tested methods. The proposed PSQ method is fully general, in that it can simulate all other memory-based quantizers if it is allowed unlimited complexity

Predictive vector quantization of wideband LSF using narrowband LSF for bandwidth scalable coders

For implementing a bandwidth-scalable coder, a wideband line spectral frequency (LSF) quantizer was developed. It works in combination with a narrowband LSF quantizer. A new predictive vector quantization was introduced to the wideband LSF quantizer. The predictive vector quantizer is based on the use of several predictive contributions, which include first-order auto regressive (AR) prediction and vector quantization (VQ) codebook mapping. One feature of the new predictive vector quantizer is exploitation of the correlation between wideband and narrowband LSFs quantized in the previous frame for estimating wideband LSF in the current frame. A 16-bit switched predictive three-stage vector quantizer was used to encode estimation residues. Results showed that introduction of the predictor brought about a performance improvement of 0.3 dB in spectral distortion. This paper describes procedures of designing the predictor and the three-stage codebook, as well as simulation results.

Predictive vector quantization of 3-D mesh geometry by representation of vertices in local coordinate systems

In predictive 3-D mesh geometry coding, the position of each vertex is predicted from the previously coded neighboring vertices and the resultant prediction error vectors are coded. In this work, the prediction error vectors are represented in a local coordinate system in order to cluster them around a subset of a 2-D planar subspace and thereby increase block coding efficiency. Alphabet entropy constrained vector quantization (AECVQ) of Rao and Pearlman is preferred to the previously employed minimum distortion vector quantization (MDVQ) for block coding the prediction error vectors with high coding efficiency and low implementation complexity. Estimation and compensation of the bias in the parallelogram prediction rule and partial adaptation of the AECVQ codebook to the encoded vector source by normalization using source statistics, are the other salient features of the proposed coding system. Experimental results verify the advantage of the use of the local coordinate system over the global one. The visual error of the proposed coding system is lower than the predictive coding method of Touma and Gotsman especially at low rates, and lower than the spectral coding method of Karni and Gotsman at medium-to-high rates.