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.

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

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.

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.

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 NEW LBG-BASED IMAGE COMPRESSION METHOD USING DCT

In this letter, a new Linde-Buzo-Gray (LBG)-based image compression method using Discrete Cosine Transform (DCT) and Vector Quantization (VQ) is proposed. A gray-level image is firstly decomposed into blocks, then each block is subsequently encoded by a 2D DCT coding scheme. The dimension of vectors as the input of a generalized VQ scheme is reduced. The time of encoding by a generalized VQ is reduced with the introduction of DCT process. The experimental results demonstrate the efficiency of the proposed method.

A high fidelity VQ coding algorithm with region adaptive subbanding

Image subbands can be obtained by using filterbank. Traditional compression method uses direct entropy coding for each subband. After studying the energy distribution in image subbands, we proposed a vector quantization (VQ) coding algorithm to image subband. In the algorithm, vector quantizers were adaptively designed for high-frequency bands in an image. In particular, the edges of the image were examined and fewer bits were assigned to high-energy regions. The experimental result showed that the algorithm had higher SNR and higher compression ratio than possible by traditional subband coding, JPEG and JPEG 2000.

A Codebook Design Method for Robust VQ-Based Face Recognition Algorithm

In this paper, we present a theoretical codebook design method for VQ-based fast face recognition algorithm to im-prove recognition accuracy. Based on the systematic analysis and classification of code patterns, firstly we theoretically create a systematically organized codebook. Combined with another codebook created by Kohonen’s Self-Organizing Maps (SOM) method, an optimized codebook consisted of 2×2 codevectors for facial images is generated. Experimental results show face recognition using such a codebook is more efficient than the codebook consisted of 4×4 codevector used in conventional algorithm. The highest average recognition rate of 98.6% is obtained for 40 persons’ 400 images of publicly available face database of AT&T Laboratories Cambridge containing variations in lighting, posing, and expressions. A table look-up (TLU) method is also proposed for the speed up of the recognition processing. By applying this method in the quantization step, the total recognition processing time achieves only 28 msec, enabling real-time face recognition.

Fast compression of computer-generated holographic images based on a GPU-accelerated skip-dimension vector quantization method

A method for fast and low bit-rate compression of digital holograms based on a new vector quantization （VQ） method known as the skip-dimension VQ （SDVQ） is proposed. Briefly, a complex hologram is converted into a real off-axis hologram, and partitioned into a set of image vectors. The image vectors are passed into a graphic processing unit （GPU）, and compressed through SDVQ into a set of code indices considerably smaller in data size than the source hologram. Experimental evaluation reveals that our scheme is capable of compressing a digital hologram to a compression ratio of over 500 times, in approximately 20-22 ms.

PDVQ图像编码系统的算法研究

PDVQ图像编码系统首先将码书进行方向性分类,把每类方向性码书中的码字按码字和值进行升序排列,并根据EBNNS算法将码书分块。编码时,先根据输入图像块的相关性进行PDVQ编码,然后分析输入图像块的方向性来选择相应的分类子码书,在该子码书中根据输入图像块的和值确定码字搜索范围,最后在确定的搜索范围内搜索最匹配码字。仿真结果表明,该系统集合了动态图像块划分(PDVQ)、基于方向性分类编码和等和值块扩展最近邻码字搜索(EBNNS)三种算法的优点,在保证重建图像质量前提下,缩短了编码时间,并提高了压缩比。

一种快速高效的VQ初始码书设计方法

提出了一种快速高效的ＶＱ初始码书生成方法——自适应决策导向聚类算法．该算法通过自适应地调整保护区的上下限，逐渐地确定了聚类的最小失真阈值和形心间的最佳距离．实验结果表明：生成的初始码书的典型性较好，在后期ＬＢＧ训练中收敛较快，在码率为０．５ｂｐｐ情况下，重构图像的峰值信噪比为３０～３６ｄＢ，建议算法的码书性能优于其他方法．

基于动态SMVQ的图像压缩算法

SMVQ利用相邻图像块之间的相关性来提高编码图像质量。然而,传统的SMVQ当图像边缘位于当前编码图像块右下角时会出现编码误差,为此,研究并提出了一种改进的SMVQ。新算法利用图像所有图像块之间的相关性对当前编码图像块进行预测。对每一个输入图像块,利用边匹配方法从已编码数据中寻找最近块,并用其对输入图像块进行预测,或产生动态码书。然后利用最近块、动态码书或总码书对输入数据进行编码,扩展了编码搜索范围,提高了图像质量。实验结果表明新算法大大降低了比特速率,而且编码图像有较好的视觉效果。

基于多级SMVQ的图象编码算法

边缘匹配矢量量化器(SMVQ)是有限状态矢量量化器(FSVQ)的一个分支。该量化器适合于对图像块间相关性高的图像进行压缩编码,其优点是在比特率相近的情况下,编码质量高于传统的穷尽搜索矢量量化编码器,但其缺点是计算量大和比特率固定。本文提出了一种改进的边缘匹配矢量量化器。测试结果表明,该算法是变比特率编码算法,它比边缘匹配矢量量化器的比特率低,编码速度快,编码质量得到提高。

PDVQ图像压缩芯片的设计与实现

研制了基于图像块动态划分矢量量化的图像压缩芯片。编码前芯片预测系统可根据图像平滑程度及相邻图像块的空间相关度自动调节待压缩的子图像块尺寸,在保证恢复图像画质的前提下,与恒定图像块尺寸矢量量化相比,图像压缩率平均提高27%,最大提高64%。芯片中码书为256阶16维矢量,并采用方向性分类及码字和值升序排列结构,有效减小了码字搜索范围。芯片的设计与实现基于Charter0.35μm标准CMOS工艺,最终芯片尺寸为2.08mm×2.08mm。测试结果表明,工作电压为3V时,PDVQ图像压缩芯片工作频率可达到100MHz,在该工作条件下芯片功耗为295mW,并可以满足512×512灰度图像在30frame/s下的实时编码要求。

一种针对彩色JPEG图像的安全隐写算法

为进一步增强载密彩色JPEG图像的安全性,提出一种针对彩色JPEG图像的安全隐写算法.算法把原始彩色JPEG图像的每个8×8分块进行DCT变换后,对各块中具有相同频率的R,G,B各分量的AC系数用密钥控制生成一个近似服从高斯分布的整数序列进行整除取余,将余数的LSB构成序列brbgbb,对brbgbb采用矩阵编码嵌入秘密信息.实验结果表明:与同类算法相比,该方法对彩色JPEG图像可提供较大的嵌入容量,载密彩色JPEG图像具有较好的视觉质量,且能够抵几种常见的隐写分析方法.

基于STM32F417的JPEG压缩算法优化与实现

提出了一种基于STM32F417的JPEG图像压缩算法优化实现方案。该方案针对STM32在控制和数字信号处理方面的突出特性,将单周期指令应用于离散余弦变换、量化、熵编码处理过程的复杂运算中,在单周期时钟内完成多步计算,从而提高了算法的运算速度。实验结果表明,基于STM32F417的JPEG算法优化方案,在图像压缩质量恒定的情况下,有效地提升了JPEG算法的压缩效率,降低了运算时间,提高了图像压缩的实时性。

模拟退火多级VQ编码方案及实现

在查阅和分析多级矢量量化和模拟退火技术有关文献资料的基础上 ,阐述了矢量量化最优码书的形成条件 ,并以多级矢量量化和模拟退火技术为基础 ,提出了一种基于模拟退火技术的多级矢量量化编码方案 ,该方案充分弥补了多级矢量量化和模拟退火技术在图象编码中应用的各自不足 ,并且发挥了多级矢量量化和模拟退火技术在图象编码中应用的各自优点 ,理论和实验都证明 ,该算法不仅能降低计算复杂度和码书存储量 ,而且图象恢复效果较好 .

基于多图嵌入的JPEG图像安全隐写算法

为提高秘密信息的嵌入量,同时增强载密JPEG图像的安全性,提出一种基于多图嵌入的JPEG图像安全隐写算法.算法以多幅JPEG图像构成的图像序列为载体,每3幅图像为一个单元,在每单元各幅图像相对应的8*8分块的DCT域中,对满足参数t约束的相同频率的AC系数的LSB采用矩阵编码的方式嵌入秘密信息.实验结果表明,参数t越大,嵌入的信息量越大,与同类算法相比,在嵌入率相同的情况下,该算法具有更优的抵抗通用JPEG图像隐写分析的能力,具有较好的安全性.