Lattice Vector Quantization Applied to Speech and Audio Coding

Lattice vector quantization （LVQ） has been used for real-time speech and audio coding systems. Compared with conventional vector quantization, LVQ has two main advantages： It has a simple and fast encoding process, and it significantly reduces the amount of memory required. Therefore, LVQ is suitable for use in low-complexity speech and audio coding. In this paper, we describe the basic concepts of LVQ and its advantages over conventional vector quantization. We also describe some LVQ techniques that have been used in speech and audio coding standards of international standards developing organizations （SDOs）.

A Novel Coding Method Based on Fuzzy Vector Quantization for Noised Image

In this paper a novel coding method based on fuzzy vector quantization for noised image with Gaussian white-noise pollution is presented. By restraining the high frequency subbands of wavelet image the noise is significantly removed and coded with fuzzy vector quantization. The experimental result shows that the method can not only achieve high compression ratio but also remove noise dramatically.

METHOD OF VECTOR QUANTIZATION CODING USING RECTANGULAR TRANSFORM FOR IMAGE COMPRESSION

First of all a simple and practical rectangular transform is given,and then thevector quantization technique which is rapidly developing recently is introduced.We combinethe rectangular transform with vector quantization technique for image data compression.Thecombination cuts down the dimensions of vector coding.The size of the codebook can reasonablybe reduced.This method can reduce the computation complexity and pick up the vector codingprocess.Experiments using image processing system show that this method is very effective inthe field of image data compression.

REMOTE SENSING IMAGE CODING METHOD COMBINING WAVELET TRANSFORM WITH CLASSIFIED VECTOR QUANTIZATION

A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages by DWT. The lowest frequency subimage is compressed by scalar quantization and ADPCM. The high frequency subimages are compressed by CVQ to utilize the similarity among different resolutions while improving the edge quality and reducing computational complexity. The experimental results show that the proposed scheme has a better performance than JPEG, and a PSNR of reconstructed image is 31～33 dB with a rate of 0.2 bpp.

FAST IMAGE ENCODING ALGORITHM BASED ON MEAN-MATCH CORRELATION VECTOR QUANTIZATION

A mean-match correlation vector quantizer (MMCVQ) was presented for fast image encoding. In this algorithm, a sorted codebook is generated regarding the mean values of all codewords. During the encoding stage, high correlation of the adjacent image blocks is utilized, and a searching range is obtained in the sorted codebook according to the mean value of the current processing vector. In order to gain good performance, proper THd and NS are predefined on the basis of experimental experiences and additional distortion limitation. The expermental results show that the MMCVQ algorithm is much faster than the full-search VQ algorithm, and the encoding quality degradation of the proposed algorithm is only 0.3～0.4 dB compared to the full-search VQ.

Fractal Block Coding:A New Method

Fractal image compression is a completely new method to compress images by searching and exploiting the self similarity of the whole image . Fractal Block Coding (FBC) is a practicable fractal coding scheme with annoying slow encoding speed . In this paper, we classify the image blocks by Classified Vector Quantization (CVQ) technique and present an Adaptive Block Truncation Coding (ABTC) scheme to process the midrange blocks in the image. By this method , we reduce the encoding time to one forty fifth comparing to ordinary FBC method with little change in compression ratio and a little decreased coded image quality.

AN EFFICIENT FAST ENCODING ALGORITHM FOR VECTOR QUANTIZATION

A fast encoding algorithm was presented which made full use of two characteristics of a vector, its sum and variance. In this paper, a vector was separated into two subvectors, one is the first half of the coordinates and the other contains the remaining coordinates. Three inequalities based on the characteristics of the sums and variances of a vector and its two subvectors were introduced to reject those codewords which are impossible to be the nearest codeword. The simulation results show that the proposed algorithm is faster than the improved equal average eaual variance nearest neighbor search (EENNS) algorithm.

ROTATED BARNES-WALL LATTICE BASED VECTOR QUANTIZER AND ITS APPLICATION IN IMAGE-SEQUENCE CODING

A new scheme is presented to design a rotated Barnes-Wall lattice based vector quantizer(LVQ). The construction method of the LVQ and its fast quantizing algorithm are described at first. Then gain-shape lattice vector quantizer(GSLVQ) with LVQ as shape quantizer is discussed. Finally the GSLVQ is used in image-sequence coding and good experimental results are obtained.

Combining IFS and VQ in Fractal Image Coding

A novel paradigm for fractal coding selectively corrects the fractal code for selected domain blocks with an image-adaptive VQ codebook. The codebook is generated from the initial uncorrected fractal code and is, therefore, available at the decoder. An efficient trade-off is generated between incremental performance and bit rate.

Bark-Band Residual Noise Model for Parametric Audio Coding

A Bark-band residual noise model integrated with the human hearing mechanism is proposed to efficiently complement sinusoidal model in parametric audio coding. The time-varying spectrum of the residual noise is retrieved by Bark-scale piecewise constant magnitude estimates along with random phases. In the proposed noise model, Bark bands information is obtained by short-time FFT method and window overlap-add technique is exploited to remove boundary discontinuities. SVQ is also incorporated into parameter quantization process for the low bit-rate coding demand. Simulation results and informal listening tests show that when the sinusoidal model is combined with the Bark-band noise model, better synthesis audio quality can be achieved compared with the original sinusoidal modeling audio codec.

A REAL-TIME IMPLEMENTATION OF 4.2Kb/s CELP SPEECH CODING

This paper presents a real-time implementation of 4.2Kb/s CELP speech coding on single DSP chip. An algorithm reducing search complexity for adaptive codebook is suggested; the solving method that the parameters are changed into LSP parameters is discussed. The realtime implementation process of this coding on a commercial development board with a single TMS320C30 is described.

A Novel Wavelet Image Coding Method Incorporating with Human Face Detection

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ADCTVQ: A New Method for Image Coding and Compressing

This paper presents a new method for image coding and compressing-ADCTVQ(Adptive Discrete Cosine Transform Vector Quantization). In this method, DCT conforms to visual properties and has an encoding ability which is inferior only to the best transform KLT. Its vector quantization can maintain the minimum quantization distortions and greatly increase the compression ratio. In order to improve compression efficiency, an adaptive strategy of selecting reserved region patterns is applied to preserving the high energy at the same compression ratio. The experiment results show that they are satisfactory at the compression ration ratio if greater than 20.

A Novel Low-bit-rate Speech Coding Based on Decomposition of the Pitch-cycle Waveform of the Linear Predictive Residual

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Image Coding Using Wavelet Transform and EntropyConstrained Vector Quantization with Quadtree Structure Vectors

This paper presents a new wavelet transform image coding method. On the basis of a hierarchical wavelet decomposition of images, entropy constrained vector quantization is employed to encode the wavelet coefficients at all the high frequency bands with

SFCVQ and EZW coding method based on Karhunen-Loeve transformation and integer wavelet transformation

A new hyperspectral image compression method of spectral feature classification vector quantization （SFCVQ） and embedded zero-tree of wavelet （EZW） based on Karhunen-Loeve transformation （KLT） and integer wavelet transformation is represented. In comparison with the other methods, this method not only keeps the characteristics of high compression ratio and easy real-time transmission, but also has the advantage of high computation speed. After lifting based integer wavelet and SFCVQ coding are intro- duced, a system of nearly lossless compression of hyperspectral images is designed. KLT is used to remove the correlation of spectral redundancy as one-dimensional （1D） linear transform, and SFCVQ coding is applied to enhance compression ratio. The two-dimensional （2D） integer wavelet transformation is adopted for the decorrelation of 2D spatial redundancy. EZW coding method is applied to compress data in wavelet domain. Experimental results show that in comparison with the method of wavelet SFCVQ （WSFCVQ）, the method of improved BiBlock zero tree coding （IBBZTC） and the method of feature spectral vector quantization （FSVQ）, the peak signal-to-noise ratio （PSNR） of this method can enhance over 9 dB, and the total compression performance is improved greatly.

A New Image Coding Algorithm Based on Self-Organizing Neural Network

The paper deals with a new VQ+DPCM+DCT algorithm based on Self-Organizing Feature Maps(SOFM) algorithm for image coding. In addition. a Frequency sensitive SOFM (FSOFM) has been also devel-oped. Simulation results show that a very good visual quality of the coded image at 0.252 bits/pixel is obtained.

一种进化模拟退火矢量量化图像编码新算法

模拟进化和模拟退火是解决全局优化问题的随机搜索技术 ,它们在工程领域有着广泛的应用 .本文将这两种技术有机结合 ,提出了一种进化模拟退火算法 ,并用其解决图像编码矢量量化码书设计问题 .实验表明 ,算法具有收敛速度快、码书性能佳等优点 .

基于小波变换的有损图像压缩算法研究

文章从实际应用角度出发，全面系统地阐述了小波变换基本理论，提出了一种基于小波变换的有损图像压缩算法。该算法首先利用小波变换对图像进行分解，然后根据各子图像特点对小波系数进行相应的向量量化编码，从而实现图像压缩目的。实验结果表明：该图像压缩算法的使用，可以在图像质量几乎不下降的条件下，极大地提高压缩比，改善恢复图像质量。

一种高效的基于模拟退火的LBG算法

针对传统矢量量化码书设计 L BG算法对初始码书敏感和在迭代过程中容易陷入局部极小的缺陷 ,结合模拟退火算法 ,提出了一种基于模拟退火的 L BG改进算法 ,并给出了退火过程中的扰动因子刻画、扰动策略选取、稳定性判据确定和温度下降策略等细节 .模拟实验结果表明 ,本文所提出的改进算法能够有效地回避对初始码书的敏感 ,同时在搜索性能和图像压缩后还原质量上都得到很好的改善 .