相关矢量量化图象编码算法的VLSI结构

本文提出了一种实现相关矢量量化图象编码算法的VLSI结构．该结构根据相关矢量量化编码算法，利用相邻图象块编码地址的相关性，提高编码效率，并采用特殊设计的图象边缘块处理方法，降低实现复杂度．本文详细讨论了相关矢量量化图象编／解码器各部分的VLSI实现结构，并介绍了电路设计与模拟结果．     

基于树结构矢量分类的小波图像图编码矢量量化

本文基于零树编码,矢量分类和网络编码量化的思想,提出了对小波图象采用树结构矢量组合和分类后进行网络编码矢量量化的新方法,该方法充分利用了带系统的带间和带内的相关性,分类信息上中用比特数少,对重要类矢量实行加权网络编码矢量量化,利用卷积编码扩展信号空间以增大量化信号间的欧氏距离,用维特比算法搜索最优量化序列,并采用基于人眼视觉性特性的加权均方误差准则作为失真度量和码字匹配,提高了量化增益,仿真结果表明,该方法编码计算复杂度适中,解码简单,可达到很好的压缩效果。     

采用空间矢量组合的小波图像分类矢量量化

该文提出了采用空间矢量组合对小波图像进行分类矢量量化的新方法。该方法充分利用了各高频子带系数的频率相关性和空间约束性将子带系数重组，依据组合矢量能量和零树矢量综合判定进行分类，整幅图像只需单一量化码书，分类信息占用比特数少，并采用了基于人眼视觉特性的加权均方误差准则进行矢量量化，提高了量化增益。仿真结果表明，该方法实现简单，在较低的编码率下，可达到很好的压缩效果。     

矢量量化码本的相关排序及其在预测编码中的应用

本文在研究矢量量化码本的树形结构和图象的相关性的基础上,提出了一种矢量量化树形码本的相关排序法。用这种排序法可以得到一组按一定相关性排列的矢量码本,对图象进行编码,进一步利用图象子象块之间的相关性压缩数码率。本文利用相关矢量码本对具有一定相关性的人物头象(Miss America)进行编码,与单纯用矢量量化编码相比,压缩数码率可达27％,编码后图象质量不变。     

基于量化系数均方误差准则的运动估计算法

提出了一种基于量化系数均方误差匹配准则的DCT域运动估计视频编码算法.算法中采用了一种新的运动估计匹配准则,该准则在DCT域内计算逆量化的残差均方误差值.由于该准则已考虑到量化噪声对运动残差能量的影响,因此与传统编码算法相比较,在图像质量基本不变的前提下码率更低.仿真结果显示,基于量化系数均方误差准则的DCT域运动估计算法具有较高的编码效率.     

采用方向树结构矢量组合的小波图像分类矢量量化

该文提出了采用方向树结构矢量组合对小波图像进行分类矢量量化的新方法。该方法的矢量构成结合了子带系数的方向性，充分利用了子带系数的带间和带内的相关性，按能量和活跃度进行两级分类，降低了类中矢量的内部离散度，分类信息占用比特数少，并采用了基于人眼视觉特性的加权均方误差准则进行矢量量化，提高了量化增益。仿真结果表明，该方法实现简单，可以达到很好的压缩效果。     

序号预测矢量量化器及其在图象编码中应用

本文提出一种序号预测矢量量化器的结构，与一般矢量量化器相比，它充分利用了图象极强的二维相关特性，并采用预测的方法去除冗余码字，从而在保证译码图象质量与一般矢量量化器的译码图象质量相同的前提下，压缩比可提高一倍以上。     

采用树结构矢量组合的小波图像分类矢量量化

本文提出了采用树结构矢量组合对小波图像进行分类矢量量化的新方法.该方法充分利用了子带系数的带间和带内的相关性,分类信息占用比特数少,并采用了基于人眼视觉特性的加权均方误差准则进行矢量量化,提高了量化增益.仿真结果表明,该方法实现简单,可达到很好的压缩效果.     

基于仿射变换的改进型矢量量化编码

本文提出了基于仿射变换的改进型矢量量化编码算法，并给出了两种不同的实用结构，与传统矢量量化算法相比，该方法在不需要重新训练新码本及不增加码本存储空间的情况下，降低了编码误差，使得重建图像的ＰＳＮＲ显著增加，图像的主观质量也得到很大的改善。     

提高恢复图象质量的新方法

矢量量化推向实用存在着两个主要问题,一是随维数增大呈指数上升的存储复杂度和计算复杂度;二是编码恢复的图象常出现方块效应,且这种现象随着维数的增大、比特率的减小而越趋严重。本文针对这两个问题首先提出了一种快速搜索算法,使编码速度提高了几倍至几十倍;其次又提出了一种分割矢量量化后滤波法,实验结果表明这种方法能有效地改善恢复图象质量,并且矢量量化速度也明显提高。     

Visual pattern image coding

A novel framework for digital image compression called visual pattern image coding, or VPIC, is presented. In VPIC, set of visual-patterns is defined independent of the images to be coded. Each visual pattern is a subimage of limited spatial support that is visually meaningful to a normal human observer. The patterns are used as a basis for efficient image representation; since it is assumed that the images to be coded are natural optical images to be viewed by human observers, visual pattern design is developed using relevant psychophysical and physiological data. Although VPIC bears certain resemblances to block truncation (BTC) and vector quantification (VQ) image coding, there are important differences. First, there is no training phase required: the visual patterns derive from models of perceptual mechanisms; second, the assignment of patterns to image regions is not based on a standard (norm) error criterion; expensive search operations are eliminated     

一种结合人脸检测的小波图像编码方法

本文基于人脸检测的基础上，提出了一种结合矢量量化（ＶＱ）的小波图像编码方法。该方法充分利用人眼的视觉特性，高压缩比时恢复图像仍能保持较好的主观质量。     

应用神经网络的图像分类矢量量化编码

矢量量化作为一种有效的图像数据压缩技术，越来越受到人们的重视。设计矢量量化器的经典算法ＬＢＧ算法，由于运算复杂，从而限制了矢量量化的实用性。本文讨论了应用神经网络实现的基于边缘特征分类的矢量量化技术。它是根据人的视觉系统对图象的边缘的敏感性，应用模式识别技术，在对图像编码前，以边缘为特征对图像内容分类，然后再对每类进行矢量量化。除特征提取是采用离散余弦变换外，图像的分类和矢量量化都是由神经网络完成     

An improved vector quantization method using deep neural network

To address the challenging problem of vector quantization (VQ) for high dimensional vector using large coding bits, this work proposes a novel deep neural network (DNN) based VQ method. This method uses a k-means based vector quantizer as an encoder and a DNN as a decoder. The decoder is initialized by the decoder network of deep auto-encoder, fed with the codes provided by the k-means based vector quantizer, and trained to minimize the coding error of VQ system. Experiments on speech spectrogram coding demonstrate that, compared with the k-means based method and a recently introduced DNN-based method, the proposed method significantly reduces the coding error. Furthermore, in the experiments of coding multi-frame speech spectrogram, the proposed method achieves about 11% relative gain over the k-means based method in terms of segmental signal to noise ratio (SegSNR).     

BTC-VQ-DCT hybrid coding of digital images

A hybrid BTC-VQ-DCT (block truncation coding, vector quantization, and discrete cosine transform) image coding algorithm is presented. The algorithm combines the simple computation and edge preservation properties of BTC and the high fidelity and high-compression ratio of adaptive DCT with the high-compression ratio and good subjective performance of VQ, and can be implemented with significantly lower coding delays than either VQ or DCT alone. The bit-map generated by BTC is decomposed into a set of vectors which are vector quantized. Since the space of the BTC bit-map is much smaller than that of the original 8-b image, a lookup-table-based VQ encoder has been designed to `fast encode' the bit-map. Adaptive DCT coding using residual error feedback is implemented to encode the high-mean and low-mean subimages. The overall computational complexity of BTC-VQ-DCT coding is much less than either DCT and VQ, while the fidelity performance is competitive. The algorithm has strong edge-preserving ability because of the implementation of BTC as a precompress decimation. The total compression ratio is about 10:1     

Vector quantization of image subbands: a survey

Subband and wavelet decompositions are powerful tools in image coding because of their decorrelating effects on image pixels, the concentration of energy in a few coefficients, their multirate/multiresolution framework, and their frequency splitting, which allows for efficient coding matched to the statistics of each frequency band and to the characteristics of the human visual system. Vector quantization (VQ) provides a means of converting the decomposed signal into bits in a manner that takes advantage of remaining inter and intraband correlation as well as of the more flexible partitions of higher dimensional vector spaces. Since 1988, a growing body of research has examined the use of VQ for subband/wavelet transform coefficients. We present a survey of these methods.     

Classified Vector Quantization of Images

Vector quantization (VQ) provides many attractive features for image coding with high compression ratios. However, initial studies of image coding with VQ have revealed several difficulties, most notably edge degradation and high computational complexity. We address these two problems and propose a new coding method, classified vector quantization (CVQ), which is based on a composite source model. Blocks with distinct perceptual features, such as edges, are generated from different subsources, i.e., belong to different classes. In CVQ, a classifier determines the class for each block, and the block is then coded with a vector quantizer designed specifically for that class. We obtain better perceptual quality with significantly lower complexity with CVQ when compared to ordinary VQ. We demonstrate with CVQ visual quality which is comparable to that produced by existing coders of similar complexity, for rates in the range 0.6-1.0 bits/pixel.     

Solution of assigning binary indexes to codevectors by a kind of hopfield neural network

A method of assigning binary indexes to codevectors in vector quantization (VQ) system, which is called pseudo-Gray coding, is presented in this paper by constructing a kind of Hopfield neural network. Pseudo-Gray coding belongs to joint source/channel coding, which could provide a redundancy-free error protection scheme for VQ of analog signals when the binary indexes of signal codevectors are used as channel symbols on a discrete memoryless channel. Since pseudo-Gray coding is of combinatorial optimization problems which are NP-complete problems, globally optimal solutions are generally impossible. Thus, a kind of Hopfield neural network is used by constructing suitable energy function to get sub-optimal solutions. This kind of Hopfield neural network is easily modified to solve simplified version of pseudo-Gray coding for single-bit-error channel model. Simulating experimental results show that the method introduced here could offer good performances.     

Reversible information hiding for VQ indices based on locally adaptive coding

Steganography is one of protective methods for secret communications over public networks such as the Internet. This paper proposes a novel reversible information hiding method for vector quantization (VQ) compressed images based on locally adaptive coding method. The proposed steganographic method embeds a secret message into VQ indices in an index table during the process of compressing the index table in the block-by-block manner. The experimental results show that, in average, the proposed method achieves the best visual quality of reconstructed images and the best embedding rate compared to two related works. In terms of compression rate and encoding execution time, in average, Yang et al.’s method is the best, followed by our proposed method, and then Lin and Chang’s method.