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.

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.

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.

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.

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

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.

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.

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.

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.

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

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基于小波压缩深度学习重构的多图像光学加密

为解决多图像加密算法密文体积大、加密效果差、重构效果不理想等问题,提出一种基于小波压缩和深度学习重构的多图像光学加密方法。利用小波压缩提取多图像的低频部分,将重排后的新图放入改进的FDT-DRPE光学加密系统中得到密文;利用矢量分解和螺旋相位变换克服FDT-DRPE不敏感问题;构造的L_S混沌系统提高密钥敏感性。提出新型深度学习网络模型BHCN,解决传统图像重构精度不高问题。实验结果表明,密文体积可压缩至原图的1/4,重构图像的峰值信噪比为34.57 dB,结构相似性为0.9521,与同类文献相比,速度更快,重构效果更好,安全性更高。

机器学习和深度学习在遥感影像分类中的对比研究

遥感影像分类是遥感技术应用的一个重要环节;机器学习和深度学习能够实现精确、自动化、迅速、可定义和规模化的遥感影像分类。本文选取机器学习算法支持向量机和深度学习算法卷积神经网络、深度置信网络、栈式自编码网络共计4种分类算法进行对比研究,并对支持向量机核函数的参数以及深度学习算法的神经元数量开展寻优以到达最高分类精度。实验结果表明,深度学习算法栈式自编码网络的总体分类精度最高,分类效果最好,在地物复杂多样地区开展遥感地物分类时具有较好的适用性和推广价值。

Vector quantization: a review

Vector quantization(VQ) is a very effective way to save bandwidth and storage for speech coding and image coding. Traditional vector quantization methods can be divided into mainly seven types, tree-structured VQ,direct sum VQ, Cartesian product VQ, lattice VQ, classified VQ, feedback VQ, and fuzzy VQ, according to their codebook generation procedures. Over the past decade, quantization-based approximate nearest neighbor(ANN)search has been developing very fast and many methods have emerged for searching images with binary codes in the memory for large-scale datasets. Their most impressive characteristics are the use of multiple codebooks. This leads to the appearance of two kinds of codebook: the linear combination codebook and the joint codebook. This may be a trend for the future. However, these methods are just finding a balance among speed, accuracy, and memory consumption for ANN search, and sometimes one of these three suffers. So, finding a vector quantization method that can strike a balance between speed and accuracy and consume moderately sized memory, is still a problem requiring study.

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。

改进自适应LBG矢量量化算法在干涉高光谱图像压缩中的应用

在基于点到线模型扩展LBG(linde-buzo-gray algorithm)矢量量化算法的基础上,提出了一种更为高效的新型自适应LBG矢量量化算法,并给出了该算法在干涉高光谱图像无损压缩中的实际压缩方案。该算法在LBG算法码书中利用点到线的垂线关系基础上进行了改进,执行进一步的自适应化迭代进而获得了更小的残差。将自适应LBG矢量量化算法应用于干涉高光谱LASIS(large aperture static imaging spectrome-ter)图像数据无损压缩中,实验结果表明,该方法比原有的扩展LBG矢量量化算法在无损压缩性能上有显著的提高,并且在与LASIS图像双向预测结合后,点到线模型扩展LBG矢量量化算法的压缩比相对于传统LBG算法有所下降,而采用本文提出算法的压缩比则有明显的提高。

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 .

基于时空图像分割和交互区域检测的人体动作识别方法

针对现有人体动作识别方法没有考虑到非人体目标的作用,提出一种基于时空图像分割和目标交互区域检测的人体动作识别方法。在视频流中检测出人体轮廓,并将其进行时空图像分段形成关键段区域;然后,扩展分段使其包含与人体交互的非人体目标,通过时空梯度方向直方图(HOG)和光流场方向直方图(HOF)描述符来表示关键段的静态和动态特征,并通过K-均值算法构建成码书,同时采用局部约束线性编码(LLC)技术来优化码书;最后采用非线性支持向量机(SVM)对特征进行学习并进行动作识别。实验结果表明,与现有基于兴趣点的方法相比,该方案获得了较高的动作识别率。