Modified Vector Quantization Research Articles

Accurate Reader Identification for the Arabic Holy Quran Recitations Based on an Enhanced VQ Algorithm

The Speaker identification process is not a new trend; however, for the Arabic Holy Quran recitation, there are still quite improvements that can make this process more accurate and reliable. This paper collected the input data from 14 native Arabic reciters, consisting of “Surah Al-Kawthar” speech signals from the Holy Quran. Moreover, this paper discusses the accuracy rates for 8 and 16 features. Indeed, a modified Vector Quantization (VQ) technique will be presented, in addition to realistically matching the centroids of the various codebooks and measuring systems’ effectiveness. Note that the VQ technique will be utilized to generate the codebooks by clustering these features into a finite number of centroids. The proposed system’s software was built and executed using MATLAB®. The proposed system’s total accuracy rate was 97.92% and 98.51% for 8 and 16 centroids codebooks, respectively. However, this study discussed two validation tactics to ensure that the outcomes are reliable and can be reproduced. Hence, the K-mean clustering algorithm has been used to validate the obtained results and discuss the outcomes of this study. Finally, it has been found that the improved VQ method gives a better result than the K-means method.

Modified Vector Quantization for Small-Cell Access Point Placement With Inter-Cell Interference

In this paper, we explore the small-cell uplink access point (AP) placement problem in the context of throughput optimality and provide solutions while taking into consideration inter-cell interference (ICI). First, we briefly review the vector quantization (VQ) approach and related single user throughput-optimal formulations for AP placement. Then, we investigate the small-cell case with multiple users and expose the limitations of mean squared error based VQ for solving this problem. While the Lloyd algorithm from the VQ approach is found not to strictly solve the small-cell case, based on the tractability and quality of the resulting AP placement, we deem it suitable as a simple and appropriate framework to solve more complicated problems. Accordingly, to minimize ICI and consequently enhance achievable throughput, we design two Lloyd-type algorithms, namely the Interference Lloyd algorithm and the Inter-AP Lloyd algorithm, both of which incorporate ICI in their distortion functions. Simulation results show that both of the proposed algorithms provide superior 95%-likely rate over the traditional Lloyd algorithm and the Inter-AP Lloyd algorithm yields a significant increase of up to 36.34% in achievable rate over the Lloyd algorithm.

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%.

Text-Independent Speaker Recognition in Clean and Noisy Backgrounds Using Modified VQ-LBG Algorithm

Speaker recognition is the process of identifying the proper speaker by analyzing the spectral shape of the speech signal. This process is done by extracting the desired features and matching the features of the speech signal. In this paper, we adopted the Mel frequency cepstrum coefficient (MFCC) technique for extracting the features from the speaker speech sample. These cepstrum coefficients are named as extracted features. The extracted MFCC features are given as input to the modified vector quantization via Linde–Buzo–Gray (modified VQ-LBG) process and expectation maximization (EM) algorithm. Vector quantization technique is mainly used for feature matching where a separate codebook will be generated for each speaker. The EM algorithm is utilized to develop the Gaussian mixture model–universal background model (GMM–UBM). In GMM–UBM model, k means cluster is summed up to consolidate data about the covariance structure of the information and the focuses of the inert Gaussians. From our analysis, the modified VQ-LBG algorithm gives better performance compared to the GMM–UBM model.

DSP implementation of modified variable vector quantization based image compression using DCT and synthesis on FPGA

In the extant context of data compression, numerous data reduction techniques have evolved and produced many innovative solutions. These elucidations may have resulted in further complexities during physical realizations. This research endeavor put forth experimentation outcomes in the field of Discrete Cosine Transform (DCT) based image compression using modified vector quantization and prototyping the algorithm on Digital Signal Processor (DSP) TMS320C6713 platform. In addition, such an algorithm is synthesized on Virtex5 XC5VSX50T Field Programmable Gate Array (FPGA). The performance metrics used and calculated here at algorithm level are Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR_, Compression Ratio (CR), Bits per Pixel (bpp), percentage Space Saving in accordance with modified variable vector quantization levels from 10 to 90.

Modified vector quantization algorithm to overcome the blocking artefact problem of vector quantization algorithm

Modified vector quantization algorithm to overcome the blocking artefact problem of vector quantization algorithm

Segmentation-based compression: New frontiers of telemedicine in telecommunication

This study proposes a novel segmentation-based compression scheme for new frontiers of telemedicine in telecommunication, where the aim is to improve the information transfer and communication between patients and medical staffs in telemedicine. The system mainly consists of improved watershed transform and modified vector quantization. Improved watershed transform is proposed to segment a medical image into several closed regions via region growing, which can achieve more efficient computation and classification. Modified vector quantization is proposed to analyze these regions with different compression rates according to the importance of the regions to simultaneously preserve important features and reduce the size of images. Experimental results indicate that the proposed system is robust and performs better than several previous methods. It is also suggested being suitable for the applications of telemedicine in telecommunication.

Image Compression: An approach using Wavelet Transform and Modified FCM

In recent past, vector quantization has been observed as an efficient technique for image compression. In general, image compression reduces the number bits required to represent an image. The main significance of image compression is that the quality of the image is preserved. This in turn increases the storage space and thereby the volume of the data that can be stored. Image compression is the application of data compression technique on digital images. Wavelet Transform based image compression remain the most common among diverse techniques proposed earlier. Waveletbased image compression provides considerable improvements in picture quality at higher compression ratios. A moment ago Artificial Neural Network has attained popularity in the field of image compression. This paper proposes a technique for image compression using modified Fuzzy CMeans (FCM) algo rithm based vector quantization (VQ). The VQ codebook is generated by a modified FCM algorithm. The principal shortcoming of standard FCM algorithm is that the objective function does not think about the spatial dependence therefore it deal with image as the same as separate points. This proposed paper modifies the standard FCM algorithm that join together both the local spatial context and the nonlocal information into the stan dard FCM cluster algorithm using a novel dissimilarity index in place of the usual distance metric. Experiments are carried out in order to estimate the performance of the proposed modified FCM algorithm in image compression using standard image set. The results exposed the performance of our approach in perspective with other conventional image compression techniques Keywords- Bits, Codebook, Neural Networks (NN), Modified Fuzzy CMeans (FCM) Algorithm, Vector Quantization (VQ), Image Compression, and Wavelet Transform

A 2-D ECG compression algorithm based on wavelet transform and vector quantization

In this paper, we proposed a new two-dimensional (2-D) wavelet-based electrocardiogram (ECG) data compression algorithm. A 1-D ECG data is first segmented and aligned to a 2-D data array, thus the two kinds of correlation of heartbeat signals can be fully utilized. And then 2-D wavelet transform is applied to the constructed 2-D ECG data array. A modified vector quantization (VQ) is employed to the wavelet coefficients. This modified VQ algorithm constructs a new tree vector (TV) which well utilizing the characteristics of the wavelet coefficients. Records selected from the MIT/BIH arrhythmia database are tested contrastively using the proposed algorithm, some compression algorithms based on wavelet transform and the other 2-D ECG compression algorithms. The experimental results show that the proposed method is suitable for various morphologies of ECG data, and that it achieves higher compression ratio with the characteristic features well preserved.

TPC data compression

In the collisions of ultra-relativistic heavy ions in fixed-target and collider experiments, multiplicities of several ten thousand charged particles are generated. The main devices for tracking and particle identification are large-volume tracking detectors (TPCs) producing raw event sizes in excess of 100 Mbytes per event. With increasing data rates, storage becomes the main limiting factor in such experiments and, therefore, it is essential to represent the data in a way that is as concise as possible. In this paper, we present several compression schemes, such as entropy encoding, modified vector quantization, and data modeling techniques applied on real data from the CERN SPS experiment NA49 and on simulated data from the future CERN LHC experiment ALICE.