Conventional Vector Quantization Research Articles

Luminance approximated vector quantization algorithm to retain better image quality of the decompressed image

Some compressed images using Vector Quantization algorithm suffers from blocking artifacts which degrades the visual appeal of the image. Present study proposes a hybrid vector quantization method applicable on de-correlated color model. As luminance channel carries image information and loss of image information results in degradation of the visual appeal of an image, so aim of this study is focused on retaining more image information during compression process. For luminance channel compression, a new four level quantization based compression method is developed. Luminance channel is partitioned into smaller blocks. Then for each block, four level quantization is applied which local to the current block only. This results many level luminance value effectively for the whole image. It helps to retain better information. Chrominance channels are compressed using conventional Vector Quantization. This hybrid compression method improves visual quality of the decompressed image reasonably compared to VQ. The proposed method is applied on many standard images found in literature and images of UCIDv.2 color image database. Results are analyzed in terms of Peak Signal to Noise Ratio, Structure Similarity Index and space requirement reduction for compressed image using the method. Experimental results show that proposed method retains better quality of image in terms of PSNR and SSIM than Vector Quantization and Modified Vector Quantization. This method reduces storage space requirement for the compressed images in the range of 84% to 89%.

PSO–SOM Neural Network Algorithm for Series Arc Fault Detection

Self-organizing feature map (SOM) neural network is a kind of competitive neural network with unsupervised learning. It has the strong abilities of self-organization and self-learning. However, the classification accuracy of SOM neural network may decrease when the features of tested object are not obvious. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the weight values of SOM network. Three indexes, i.e., intra-class density, standard deviation and sample difference, are used to judge the weight value, which can improve the classification accuracy of the SOM network. PSO–SOM network is applied to the detection of series arc fault in electrical circuits and compared with conventional SOM network and learning vector quantization (LVQ) network. The detection accuracy of the PSO–SOM network is 95%, which is higher than conventional SOM network and LVQ network.

A scalable speech coding scheme using compressive sensing and orthogonal mapping based vector quantization

A novel scalable speech coding scheme based on Compressive Sensing (CS), which can operate at bit rates from 3.275 to 7.275 kbps is designed and implemented in this paper. The CS based speech coding offers the benefit of combined compression and encryption with inherent de-noising and bit rate scalability. The non-stationary nature of speech signal causes the recovery process from CS measurements very complex due to the variation in sparsifying bases. In this work, the complexity of the recovery process is reduced by assigning a suitable basis to each frame of the speech signal based on its statistical properties. As the quality of the reconstructed speech depends on the sensing matrix used at the transmitter, a variant of Binary Permuted Block Diagonal (BPBD) matrix is also proposed here which offers a better performance than that of the commonly used Gaussian random matrix. To improve the coding efficiency, formant filter coefficients are quantized using the conventional Vector Quantization (VQ) and an orthogonal mapping based VQ is developed for the quantization of CS measurements. The proposed coding scheme offers the listening quality for reconstructed speech similar to that of Adaptive Multi rate - Narrowband (AMR-NB) codec at 6.7 kbps and Enhanced Voice Services (EVS) at 7.2 kbps. A separate de-noising block is not required in the proposed coding scheme due to the inherent de-noising property of CS. Scalability in bit rate is achieved in the proposed method by varying the number of random measurements and the number of levels for orthogonal mapping in the VQ stage of measurements.

Improving Performance of Digital Mobile Fronthaul Employing 2-D Vector Quantization With Vector Linear Prediction

In this paper, a two-dimensional (2-D) vector quantization with vector linear prediction (VLP-VQ) is proposed to improve the transmission performance of the digital mobile fronthaul (MFH). Compared with the conventional vector quantization based on k-means clustering algorithm, the VLP technique can decrease the mean square error of the orthogonal frequency division multiplexing (OFDM) samples by around 5-50% and thereby greatly reduce the quantization noise at the transmitter. In the proof-of-concept experiment, the proposed scheme is demonstrated in a 25-km 10 Gbaud/λ four-level pulse amplitude modulation intensity modulation and direct detection system. In the 5G new radio environment, the experimental results proved the ability of the proposed digital MFH to support 27, 20, 16, and 13 100-MHz OFDM with 4, 16, 64, and 256 quadrature amplitude modulation based antenna-carrier containers at quantization bits per sample of 3, 4, 5, and 6. Compared with the conventional 2-D vector quantization, the error vector magnitude performance of the proposed scheme is significantly improved with the same number of quantization bits.

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.

Hybrid Precoding Architecture for Massive Multiuser MIMO With Dissipation: Sub-Connected or Fully Connected Structures?

In this paper, we study the hybrid precoding structures over limited feedback channels for massive multiuser multiple-input multiple-output (MIMO) systems. We focus on the system performance of hybrid precoding under a more realistic hardware network model, particularly, with inevitable dissipation. The effect of quantized analog and digital precoding is characterized. We investigate the spectral efficiencies of two typical hybrid precoding structures, i.e., the sub-connected structure and the fully-connected structure. It is revealed that increasing signal power can compensate the performance loss incurred by quantized analog precoding. In addition, by capturing the nature of the effective channels for hybrid processing, we employ a channel correlation-based codebook and demonstrate that the codebook shows a great advantage over the conventional random vector quantization (RVQ) codebook. It is also discovered that, if the channel correlation-based codebook is utilized, the sub-connected structure always outperforms the fully-connected structure in either massive MIMO or low signal-to-noise ratio (SNR) scenarios; otherwise, the fully-connected structrue achieves better performance. Simulation results under both Rayleigh fading channels and millimeter wave (mmWave) channels verify the conclusions above.

Perceptually Weighted Analysis-by-Synthesis Vector Quantization for Low Bit Rate MFCC Codec

This letter presents a perceptually weighted analysis-by-synthesis vector quantization (VQ) algorithm for low bit rate MFCC codec. Different from conventional VQ of mel-frequency cepstral coefficients (MFCCs) vector, this algorithm uses an analysis-by-synthesis technique and aims to minimize the perceptually weighted spectral reconstruction distortion rather than the distortion of MFCCs vector itself. Also, to reduce the computational complexity, we propose a practical suboptimal codebook searching technique and embed it into the split and multistage VQ framework. Objective and subjective experimental results on Mandarin speech show that the proposed algorithm yields intelligible and natural sounding speech for speech coding at 600–2400 bit/s. Compared to current VQ in MFCC codec, the output speech quality is substantially improved in terms of frequency-weighted segmental SNR, short-time objective intelligibility score, perceptual evaluation of speech quality score, and mean opinion score.

Image Compression Based on Discrete Cosine Transform and Multistage Vector Quantization

In this paper, an image compression scheme is proposed, based on discrete cosine transform (DCT). This scheme is a hybrid method, which combines vector quantization (VQ) and differential pulse code modulation (DPCM). This scheme begins with transforming image from spatial domain to frequency domain using DCT. Then the block data is transformed into a vector according to zigzag order, and then truncated. After that, the vector is split into DC coefficient and AC coefficients. After scale quantization, DC coefficient is coded using DPCM. AC coefficients are coded using multistage vector quantization (MSVQ). Then, entropy encoding is performed on index-tables and DC part, separately. The experimental results show that, compared to conventional VQ and DCTVQ schemes, proposed scheme has a better performance.

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.

Line spectral frequencies modeling by a mixture of von Mises–Fisher distributions

Efficient quantization of the linear predictive coding (LPC) parameters plays a key role in parametric speech coding. The line spectral frequency (LSF) representation of the LPC parameters has found its applications in speech model quantization. In practical implementations of vector quantization (VQ), probability density function optimized VQ has been shown to be more efficient than the VQ based on training data. In this paper, we present the LSF parameters by a unit vector form, which has directional characteristics. The underlying distribution of this unit vector variable is modeled by a von Mises–Fisher mixture model (VMM). An optimal inter-component bit allocation strategy is proposed based on high rate theory and a distortion-rate (D-R) relation is derived for the VMM based-VQ (VVQ). Experimental results show that the VVQ outperforms the recently introduced Dirichlet mixture model-based VQ and the conventional Gaussian mixture model-based VQ, in terms of modeling performance and D-R relation.

A novel legitimacy preserving data hiding scheme based on LAS compressed code of VQ index tables

Data hiding is a technique for secret and secure data storing and transmission that embeds data into a media such as an image, audio, video and so on, with minimal quality degradation of the media. Some developed data hiding schemes are reversible. Reversibility property allows the media to be recovered completely after extraction of the embedded data. Vector Quantization (VQ)-based image data hiding is one of the most popular study areas in the literature. However, most VQ-based reversible data hiding schemes generate non-legitimate codes as output. In other words output codes generated by such schemes could not be decoded by the conventional VQ or VQ based decoders and may arouse the attention of interceptors. On the other hand, the existing VQ based reversible data hiding schemes that generate legitimate VQ codes as output, suffer from low capacity and poor quality of stego-image. In this paper a novel reversible data hiding scheme for VQ-compressed images based on locally adaptive data compression scheme (LAS) is proposed. Unlike other schemes, the proposed scheme doesn't change the VQ indices; data is embedded by choosing one of the possible ways to encode each index. As a result, in comparison with the schemes that embed data by index replacement, in the proposed scheme no extra distortion is made by data embedding and the outputted codes are compatible with the conventional LAS decoder. These properties help to hide the existence of secret data and make the scheme suitable for steganography. Moreover, a framework to combine the proposed scheme with some other schemes to improve their capacity and embedding side information is proposed. Since LAS is a general data compression scheme, the proposed scheme could be used to embed data into any data formats. All existing LAS based data hiding schemes produce non-legitimate codes as their outputs and the proposed scheme is the first and only one that produces legitimate codes as output. Experimental results show that the proposed scheme outperforms the existing LAS based schemes and some other VQ based data hiding schemes. On average, the proposed scheme embeds 2.14 bits per index with almost the same bit-rate as the bit-rate of the VQ index table.

Omnifont text recognition of printed cursive scripts via HMMs, compact lossless features, and soft data clustering

This paper presents an optical character/text recognition (OCR) system for cursive scripts like those of Arabic, Urdu, Persian, Kurdish, etc. This OCR system is a large-scale one in the sense of architecture, training data size, and state-of-the-art performance. The paper introduces the theoretical derivation and experimental assessment of our two main contributions deployed in this OCR. The first contribution is the design of a new autonomously normalized, lossless, and compact feature vector that enables the production of a truly robust omnifont OCR system for cursive scripts with an ASR-like HMM-based architecture. Half of the components in this feature vector are analogs and the other half are discrete, which obstructs the use of continuous Gaussian mixtures to model an aggregate of such features and mandates the use of discrete HMMs instead, which in turn necessitates the deployment of vector clustering and quantization. The second contribution is a new soft (i.e., probabilistic) vector quantization (VQ) scheme, as opposed to conventional hard-deciding VQ that we analytically derive and then deploy to alleviate overfitting and boost the robustness of our OCR against different kinds of obtrusive variances. We present experimental evidence of these benefits of the presented soft VQ scheme to our OCR. Other machine-learning systems with VQ modules may also deploy soft VQ to obtain the same benefits.

Index compression for vector quantization using principal index-pattern coding algorithm

This paper presents an efficient lossless compression algorithm, the coding tree assignment scheme with principal index-pattern coding algorithm (CTAS-PIPCA), to encode image vector quantization (VQ). The coding model is designed on the basis of the schemes proposed in the previous works to further improve the coding performance of coding tree assignment scheme with improved search-order coding algorithm (CTAS-ISOC) by PIPCA. The PIPCA technique exploits the correlation of neighboring index pairs not in the original vector-quantized index map but in the principal index-pattern table which is generated from the two-dimensional histogram of index patterns in the training stage. The CTAS-PIPCA method is evaluated via extensive experiments. The searching matched index in the principal index-pattern table results in lower time complexity than CTAS-ISOC. The results also show that the proposed technique apparently reduces the bit rate as compared to the conventional VQ and other existing popular lossless index coding schemes, such as SOC and CTAS-ISOC.

Application of compressive sensing to limited feedback strategy in large‐scale multiple‐input single‐output cellular networks

A novel limited feedback strategy based on a compressive sensing technique is proposed for the downlink large-scale multiple-input single-output (MISO) system. The effectiveness of transmit beamformers relies on the feedback quality of channel state information (CSI). The instantaneous CSI is compressed and sent back to the base station (BS) by receivers. An efficient recovery algorithm is applied at the BS in the presence of measurement noise and feedback link noise. The proposed feedback strategy can provide the BS with much more precise CSI as compared with some conventional vector quantisation schemes. The proposed scheme also has lower-computational complexity. Furthermore, the authors derive a tight lower bound on the user sum rate, assuming that zero-forcing beamforming is applied at the BS, and that active users are semi-orthogonal to each other. Numerical results show that the proposed compressive sensing-based limited feedback scheme yields better performance gain than the conventional channel quantisation approaches.

Index compression for vector quantisation using modified coding tree assignment scheme

Recently, a lossless vector quantisation (VQ) index compression algorithm, called the coding tree assignment scheme with improved search-order coding (CTAS-ISOC) algorithm, has been proposed to enhance the coding efficiency of the original SOC by exploiting the correlations of the neighbouring blocks using the left-pair and upper-pair patterns in the index domain. This study proposes a modified coding tree assignment scheme (MCTAS) to further improve the coding performance of CTAS-ISOC by the dynamic index table coding (DITC). The DITC technique exploits the correlation of neighbouring index pairs not in the original vector-quantised index map, but in the temporarily constructed index table that has been classified and updated for each current index. The searching matched index in a previously qualified index table results in low-time complexity. In addition, the identical index table can be regenerated in the reconstruction process of the index map at the decoder end. Experimental results show the time complexity of MCTAS is more efficient than that of CTAS-ISOC. Moreover, the proposed MCTAS apparently reduces the bit rate in comparison with conventional VQ and some popular lossless index coding schemes, such as index searching algorithm with index associated list, adaptive index coding scheme, SOC and so on.

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.

Low Bit-Rate Encoding Algorithm for Distributed Speech Recognition Based on SVD Decomposition

The paper presents an algorithm for compression of front-end feature extracted parameters used in Distributed Speech Recognition (DSR). In the proposed method the source encoder is mainly based on truncated Singular Value Decomposition transform (SVD) with conventional vector and scalar quantizers. The system provides a compression bit-rate around 3500 bps. The experiments were carried out on the TIDigitsAurora-2 database using Hidden Makcov Model Toolkit (HTK). The simulation results show good recognition performance without dramatic change, comparing toconventional ETSI Aurorastandard front-end feature compression algorithm with quantized features at 4400 bps.

Image Vector Quantization codec indexes filtering

Vector Quantisation (VQ) is an efficient coding algorithm that has been widely used in the field of video and image coding, due to its fast decoding efficiency. However, the indexes of VQ are sometimes lost because of signal interference during the transmission. In this paper, we propose an efficient estimation method to conceal and recover the lost indexes on the decoder side, to avoid re-transmitting the whole image again. If the image or video has the limitation of a period of validity, re-transmitting the data wastes the resources of time and network bandwidth. Therefore, using the originally received correct data to estimate and recover the lost data is efficient in time-constrained situations, such as network conferencing or mobile transmissions. In nature images, the pixels are correlated with their neighbours and VQ partitions the image into sub-blocks and quantises them to the indexes that are transmitted; the correlation between adjacent indexes is very strong. There are two parts of the proposed method. The first is pre-processing and the second is an estimation process. In pre-processing, we modify the order of code- vectors in the VQ codebook to increase the correlation among the neighbouring vectors. We then use a special filtering method in the estimation process. Using conventional VQ to compress the Lena image and transmit it without any loss of index can achieve a PSNR of 30.429 dB on the decoder. The simulation results demonstrate that our method can estimate the indexes to achieve PSNR values of 29.084 and 28.327 dB when the loss rate is 0.5% and 1%, respectively.

High-Rate Vector Quantization for Noisy Channels With Applications to Wideband Speech Spectrum Compression

This paper considers the high-rate performance of source coding for noisy discrete symmetric channels with random index assignment (IA). Accurate analytical models are developed to characterize the expected distortion performance of vector quantization (VQ) for a large class of distortion measures. It is shown that when the point density is continuous, the distortion can be approximated as the sum of the source quantization distortion and the channel-error induced distortion. Expressions are also derived for the continuous point density that minimizes the expected distortion. Next, for the case of mean squared error distortion, a more accurate analytical model for the distortion is derived by allowing the point density to have a singular component. The extent of the singularity is also characterized. These results provide analytical models for the expected distortion performance of both conventional VQ as well as for channel-optimized VQ. As a practical example, compression of the linear predictive coding parameters in the wideband speech spectrum is considered, with the log spectral distortion as performance metric. The theory is able to correctly predict the channel error rate that is permissible for operation at a particular level of distortion.

Color reduction using a multi-stage Kohonen Self-Organizing Map with redundant features

Reducing the number of colors in an image while preserving its quality, is of importance in many applications such as image analysis and compression. It also decreases the memory and transmission bandwidth requirements. Moreover, classification of image colors is applicable in image segmentation, and object detection and separation, as well as producing pseudo-color images. In this paper, the Kohonen Self-Organizing Map Neural Network is employed to form an adaptive color reduction method. To enhance the performance of this method, we have used redundant features obtained by one-to-one functions from three main components of the color image (e.g. Red, Green and Blue channels). Exploiting these features will increase the color discrimination and details illustration ability of the network compared to the conventional approaches. This method leads to satisfactory results in image segmentation, especially in small object detection problems. It is also investigated that if the number of features in Kohonen network grows even by using non-deterministic one-to-one functions, the network revenue considerably improves. Moreover, we will study the effect of various adaptation algorithms in Kohonen network training stage. Again, using a multi-stage color reduction procedure which employs both Kohonen neural networks and conventional vector quantization schemes improves the performance. Several experimental results are represented to illustrate the characteristics of different approaches.