Multistage Vector Quantization Research Articles

Video compression based on Multiwavelet and Multi Stage Vector Quantization using Adaptive Diamond Refinement Search Algorithm

Due to the advances in the digital technology, multimedia processing has become the essential requirement in many applications. These applications find wide use in mobile, personal computer(PC), TV, surveillance and satellite broadcast. Also it is necessary that the video coding algorithms to be updated in order to meet the requirements of latest hardware devices. The processing speed and bandwidth are essential parameters in these applications. A good video compression standard can achieve these parameters adequately. In the proposed system, the video coding standard is implemented using the three important stages. In which the first sage uses multiwavelets to achieve good compression rate. Also it reduces the memory and bandwidth requirement. Second stage is the Multi Stage Vector Quantization(MVSQ) which reduces the complexity of searching process and the size of codebook. Third stage uses Adaptive Diamond Refinement Search(ADRS) algorithm for the motion estimation which has better performance than the Adaptive Diamond Orthogonal Search(ADOS) and Diamond Refinement Search(DRS) algorithms. The combination of multiwavelet, Multi Stage Vector Quantization(MVSQ) and Adaptive Diamond Refinement Search(ADRS) algorithm gives the high compression ratios. Preliminary results indicate that the proposed method has good performance in terms of average number of search points, PSNR values and compression rates.

Design and Comparison of Vector Quantization Codebooks for Narrowband Speech Coding

Vector quantization codebook algorithms are used for coding of narrow band speech signals. Multi-stage vector quantization and split vector quantization methods are two important techniques used for coding of narrowband speech signals and these methods are very popular due to the high bit rate minimization during coding of the signals. This paper presents performance measurements of multistage vector quantization and split vector quantization methods. We used line spectral frequencies for coding of the speech signals in codebook tables so as to ensure filter stability after quantization. The codebooks were generated by using the Linde-Buzo-Gray (LBG) algorithm. The tests were performed by selecting large amount of input data in training and test stages and to evaluate noise robustness of the methods, both noisy and clean speech signals were used. As a result, different codebooks were designed and tested in many stages and different bit rates to measure quantization performance. It is measured in terms of spectral distortion evaluation. We obtained the best result in 24bit multistage vector quantization codebook with a spectral distortion less than 1 dB for clean speech training data input. When we compared multistage and split vector quantization codebook spectral distortion results, multistage codebooks gave better performance in each option.

A Novel and Efficient Vector Quantization Based CPRI Compression Algorithm

The future wireless network, such as the Centralized Radio Access Network (C-RAN), will need to deliver data rate about 100–1000 times the current fourth-generation (4G) technology. For the C-RAN-based network architecture, there is a pressing need for tremendous enhancement of the effective data rate of the common public radio interface (CPRI). Compression of CPRI data is one of the potential enhancements. In this paper, we introduce a vector quantization based compression algorithm for CPRI links, utilizing the Lloyd algorithm. Methods to vectorize the I/Q samples and enhanced initialization of the Lloyd algorithm for codebook training are investigated for improved performance. Multistage vector quantization and unequally protected multigroup quantization are considered to reduce codebook search complexity and codebook size. Simulation results show that our solution can achieve compression of four times for uplink and 4.5 times for downlink, within $2\%$ error vector magnitude (EVM) distortion. Remarkably, vector quantization codebook proves to be quite robust against data modulation mismatch, fading, signal-to-noise ratio (SNR), and Doppler spread.

Analysis of lossy to near-lossless compression of hyperspectral imagery using prediction and multi-stage vector quantisation

ABSTRACTCompression of hyperspectral images on-board is being pursued diligently for the past decade due to the exhaustive applications in the arena of remote sensing. Compression algorithms have to be designed in the interest of using minimum number of bits to represent a pixel at the cost of maintaining the quality of the reconstructed signal. Referring to the above context, compression algorithm has been proposed based on multi-stage vector quantisation affirming a bit rate as low as possible, meanwhile preserving the quality of the signal. The spectral redundancy is well exploited in the pre-processing stage, the occurrence of which aids the functioning of vector quantisation, resulting in the lower bit rate in the subsequent stages of decorrelation. The proposed algorithm reforms the vector quantisation in terms of spectral distortion, complexity and memory requirements. The proposed strategy divulges with a peak signal to noise ratio of 82.55 dB and signal to distortion noise ratio of 92.23 dB.

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.

Removal of Real World Noise in Speech: Comparision of Various Parameters Using Kalman and H-Infinity Filter Algorithms

Background/Objectives: The performance of the speech enhancement techniques is examined by applying them to the speech signals corrupted by real world noise. Methods: An advanced coding Methodology is introduced to reduce the noisy signal using H-infinity filter. Here two compression techniques - Switch Split Vector Quantization (SSVQ) and Multi Stage Vector Quantization (MSVQ) are used. The enhancement techniques like Kalman Filter and Recursive Filter were assessed for similarities and differences with H-Infinity Filter and the outcomes are compared using signal to noise ratio which is likely to be affected on real world noise. Findings: In this paper using kalman filter, recursive filter, h-infinity filter plays vital role for comparing the various parameter characteristics. Kalman filter uses ordered steps that solve a mathematical problem. H-infinity filter differs from the normal changed Kalman filtering as it requires the knowledge of commotion parameters. H-infinity minimizes the estimation errors and thus obtains robustness and obtains the better results in enhancement. Application/Improvements: This study can be helpful for improving the intelligibility in speech signal and the same method can be implemented with different types of hybrid vector quantization techniques and may achieve better SNR and quality of the signal.

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.

Entropy-constrained reflected residual vector quantization: A realization of large block vector quantization

Vector quantization (VQ) techniques have been used for a number of years for developing successful data compression and classification algorithms. Vector quantization is inherently an unsupervised data clustering technique that smartly adopts to the given data statistics and is thus used in large variety of tasks. The gains associated with a VQ system are largely linked to the size of the block used. However, the complexity increases exponentially with an increase in block length. In conventional methods, a dimensional reduction pre-processing step is invoked before the VQ system in order to reduce the block length. For some data sources this may be unacceptable approach such as with hyper-spectral data. Therefore, a large block VQ is needed to fully exploit the linear and non-linear correlation in a data source. We propose an entropy-constrained reflected residual VQ (EC-RRVQ) as one alternative for large block vector quantization. The reflected residual VQ, a type of multistage VQ, has constrained structure with multiple stages having small-size code-books. The use of multiple code-books and a reflection constraint makes the computational complexity linearly depend on the number of stages involved. The linear increase in complexity prompted us to pose EC-RRVQ as a contender in the list of options of large block vector quantization implementation algorithms. Experimental results indicate that good image reproduction quality can be accomplished at relatively low bit rates. The performance of a 64-stage 16×16 EC-RRVQ at 0.175 bits per pixel is 23.75dB with 96 vector distortion calculations per source vector, while the number for previously proposed large-dimensional entropy-constrained residual VQ (EC-RVQ) designed under the same specifications is 21.17dB with 1212 vector distortion calculations per source vector.

Joint Source-Channel Vector Quantization for Compressed Sensing

We study joint source-channel coding (JSCC) of compressed sensing (CS) measurements using vector quantizer (VQ). We develop a framework for realizing optimum JSCC schemes that enable encoding and transmitting CS measurements of a sparse source over discrete memoryless channels, and decoding the sparse source signal. For this purpose, the optimal design of encoder-decoder pair of a VQ is considered, where the optimality is addressed by minimizing end-to-end mean square error (MSE). We derive a theoretical lower bound on the MSE performance and propose a practical encoder-decoder design through an iterative algorithm. The resulting coding scheme is referred to as channel-optimized VQ for CS, coined COVQ-CS. In order to address the encoding complexity issue of the COVQ-CS, we propose to use a structured quantizer, namely low-complexity multistage VQ (MSVQ). We derive new encoding and decoding conditions for the MSVQ and then propose a practical encoder-decoder design algorithm referred to as channel-optimized MSVQ for CS, coined COMSVQ-CS. Through simulation studies, we compare the proposed schemes vis-à-vis relevant quantizers.

Low bit-rate multi stage vector quantization based on energy clustered training set

In this paper, a new multi stage vector quantization with energy clustered training set is proposed for color image coding. The input image is applied with orthogonal polynomials based transformation and the energy clustered transformed training vectors are obtained with reduced dimension. The stage-by-stage codebook for vector quantization is constructed from the proposed transformed training vectors so as to reduce computational complexity. This method also generates a single codebook for all the three color components, utilizing the inter-correlation property of individual color planes and interactions among the color planes due to the proposed transformation. As a result, the color image encoding time is only slightly higher than that of gray scale image coding time and in contrast to the existing color image coding techniques, whose time is thrice greater than that of gray scale image coding. The experimental results reveal that only 35 % and 10 % of transform coefficients are sufficient for smaller and larger blocks respectively, for the reconstruction of images with good quality. The proposed multi stage vector quantization technique is faster when compared to existing techniques and yields better trade-off between image quality and block size for encoding.

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.

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.

Hidden data transmission in mixed excitation linear prediction coded speech using quantisation index modulation

In this study novel data hiding methods that embed secret data during mixed excitation linear prediction (MELP) coding of the speech signal are proposed. The secret data bits are hidden by using quantisation index modulation (QIM) which is carried out in the multistage vector quantisation (MSVQ) of line spectral frequencies (LSF) parameters. The distortion rates, introduced by the proposed data hiding methods, are compared with that of a previously published dither-like data hiding method by employing tests performed on the speech files selected from NTIMIT database. Weighted Euclidian distances and spectral distortion are calculated as objective distortion quality metrics, whereas perceptual evaluation of speech quality, PESQ P.862, which tries to mimic mean opinion scores, is used to give subjective listening test results. After the calculation of objective and subjective quality metrics, it is determined that the methods with QIM at the third and fourth stages of the MSVQ distort speech only as much as that resulting from a 10 -3 bit error rated (BER) additive white Gaussian noise (AWGN) channel. Later, steganographic strengths of some selected QIM methods are elaborated by the steganalysis methods trained with the computed chaotic features. According to the steganalysis results, methods that apply QIM on the third and fourth indexes of MELP-MSVQ assure both lower distortion and better steganographic imperceptibility.

An efficient fingerprint image compression technique based on wave atoms decomposition and multistage vector quantization

Modern fingerprint image compression and reconstruction standards used by the US Federal Bureau of Investigation (FBI) are based upon the popular biorthogonal 9-7 discrete wavelet transform. Multiresolution analysis tools have been successfully appli

Adaptive Long-Term Coding of LSF Parameters Trajectories for Large-Delay/Very- to Ultra-Low Bit-Rate Speech Coding

This paper presents a model-based method for coding the LSF parameters of LPC speech coders on a basis, that is, beyond the usual 20-30 ms frame duration. The objective is to provide efficient LSF quantization for a speech coder with large delay but very- to ultra-low bit-rate (i.e., below 1 kb/s). To do this, speech is first segmented into voiced/unvoiced segments. A Discrete Cosine model of the time trajectory of the LSF vectors is then applied to each segment to capture the LSF interframe correlation over the whole segment. Bi-directional transformation from the model coefficients to a reduced set of LSF vectors enables both efficient sparse coding (using here multistage vector quantizers) and the generation of interpolated LSF vectors at the decoder. The proposed method provides up to 50% gain in bit-rate over frame-by-frame quantization while preserving signal quality and competes favorably with 2D-transform coding for the lower range of tested bit rates. Moreover, the implicit time-interpolation nature of the long-term coding process provides this technique a high potential for use in speech synthesis systems.

Constrained Voicing-Based Codebook in Low-Rate Wideband CELP Coding

In this work we propose an efficient technique to quantize the immitance spectral frequency (ISF) in an algebraic code-excited linear prediction (ACELP) wideband codec. The Constrained Voicing-Based Vector Quantization (C-VBVQ) presented in this paper improves substantially the performance of the unconstrained-VBVQ approach for both clean and noisy speech. Both techniques reduce the codebook search time by almost one third. However, in the C-VBVQ training phase, the three codebooks that are individually designed for voiced, unvoiced and transition speech, are jointly reoptimized to impose a structural configuration. The proposed technique reduces the processing delay since it restricts the quantization of an input vector to only one smaller but optimal codebook. For each speech frame, one codebook is selected from the set of three codebooks based on the interframe correlation of the spectral information. The C-VBVQ was successfully implemented in an ACELP wideband coder. The objective and subjective performance are not only superior to that of the combination of the split vector quantization and multistage vector quantization but also to the unconstrained VBVQ.

DCT_M model for excitation parameter in low bit rate vocoder

The description precision of an excitation signal greatly influences the quality of reconstructed speech in low bit rate vocoders. To improve the reconstruction quality, the DCT_M model is proposed to express the excitation spectral parameter, which transforms the variable length vector to fixed dimension vector through DCT transformation. It then quantizes the fixed length vector using multi-stage vector quantization. Tests show that the proposed method can keep the shape of the entire spectral envelope and reduce model error thus greatly improve the description precision. Test results in the sine excitation linear prediction (SELP) vocoder show that the DCT_M model can improve the naturalness of reconstructed speech, with subjective test score of 65%.

Considerations of data handling system of a spaceborne imaging spectrometer with onboard data compression

This paper outlines the data handling system (DHS) of a spaceborne hyperspectral imager with an onboard data compressor. The data compression techniques to be used are successive approximation multistage vector quantization and hierarchical self-organizing cluster vector quantization. Considerations and implementation aspects of the DHS related to the onboard data compression are addressed. The impact of anomalies (spikes, saturation, etc.) in the raw data on compression performance is evaluated for the purpose of determining whether or not onboard data scrubbing is required before compression. The evaluation results show that anomalies in raw data have no significant effect on compression. This paper evaluates the impact of preprocessing and the conversion of raw data to radiance units on data compression using remote sensing applications to examine whether or not they should be applied on board before compression. The evaluation results show that preprocessing and radiometric conversion applied either before or after compression have no impact on an application using leaf area index but have impact on a target detection application using spectral unmixing. This paper also examines the combination of the two compression techniques to see if there is a performance improvement over a single technique. The experimental results show that the combined compression system does not perform better than either technique alone. Lastly, the resilience of the two compression techniques against bit errors caused by single event upsets is examined. The experimental results show that there is no loss of data fidelity when the error rate is ≤10–6.

Multipurpose speech watermarking based on multistage vector quantization of linear prediction coefficients

To make speech watermarking achieve both copyright protection and integrity verification, a novel multipurpose speech watermarking algorithm based on the multistage vector quantization (MSVQ) of linear prediction coefficients (LPCs) is presented in this article. The property of natural speech that the vector quantization (VQ) indices of the LPCs amongst neighboring frames tend to be very similar is utilized to embed the robust watermark in the indices of the first-stage VQ(VQ1). Then, the semi-fragile watermark is embedded in the indices of the second-stage VQ(VQ2) with index constrained VQ encoding scheme. Both the robust watermark and the semi-fragile watermark can be extracted without host speech. Simulation results verify the effectiveness of the proposed algorithm in terms of robustness and semi-fragility.

Vector quantization of mel‐cepstral coefficients using distortion measure for spectral analysis

AbstractThis paper proposes a method for vector quantization of mel‐cepstral coefficients based on a measure for spectral analysis. It is a method in which the match between the input signal within the frame and the mel‐cepstral coefficients stored in the codebook is evaluated by using a statistical measure for spectral analysis, and the quantized mel‐cepstral coefficients are derived. The codebook training algorithm for the proposed method is also presented. The proposed method is further applied to multistage vector quantization. It is shown by an experiment using a speech database that the proposed method with a codebook size of 9 bits can achieve the same quantization distortion as the conventional 12‐bit method (vector quantization based on Euclidean distance between mel‐cepstral coefficients). © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(13): 104–114, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10432