基于改进SMVQ的图像压缩算法

在图像编码方法中,矢量量化被认为是一种有效的低比特率图像编码方法.边匹配有限状态矢量量化利用相邻图像块之间的相关性避免了图像块边界之间大的灰度跃变.本文提出了一种改进的边匹配有限状态矢量量化,即双向低复杂度基于改进梯度的边匹配有限状态矢量量化.在双向低复杂度基于改进梯度的边匹配有限状态矢量量化中,第一次量化的状态码书尺寸由相邻图像块的梯度确定,第二次量化对第一次量化后的矢量中梯度值大于设定门限的矢量重新进行量化以提高图像质量.此外,和传统边匹配有限状态矢量量化利用上邻矢量和左邻矢量确定状态码书不同,新算法利用上、下、左、右四个相邻矢量来确定状态码书.试验结果表明,该算法的第二层编码在峰值信噪比上有1.5dB的改善;和传统的边匹配矢量量化相比较,在比特率相同时峰值信噪比平均有1.54dB的改善.     

基于小波变换的多级矢量量化图像编码算法

结合小波变换中多分辨率分析特性以及多级矢量量化复杂度低、量化效果较好的特点提出了一种基于小波变换的多级矢量量化图像编码方法。在使用多级量化的基础上采用联合优化多级矢量量化的码本设计方法,进一步提升量化效果。试验数据表明,该方法相对于传统的矢量量化算法,量化效果进一步提高,复杂度也在可接受范围之内,达到了很好的压缩编码效果。     

归一化自适应预测矢量量化算法压缩SAR原始数据

该文提出归一化自适应预测矢量量化(NAPVQ)算法压缩SAR原始数据。NAPVQ算法先采用矢量线性预测器对输入矢量进行预测,再对原矢量与预测矢量之间的残差矢量进行矢量量化。该算法可视为差分脉冲调制在矢量量化中的拓展,其性能优于块自适应量化(BAVQ)算法以及归一化预测自适应量化(NPAQ)算法。对算法复杂度的进一步分析表明,NAPVQ算法能获得复杂度和性能之间比较合理的折衷,具有实用价值。     

基于小波变换的最小失真预测/多级矢量量化

矢量量化器的压缩性能随维数的增大而提高,但复杂度亦随维数的增大呈指数增大,限制了大维数矢量的使用。本文利用小波变换产生的子带间的相关性,提出一种新的最小失真预测／多级矢量量化算法。一方面通过最小失真预测来降低时间复杂度,使得编码６３Ｄ的矢量只需付出相当于１５Ｄ矢量的时间复杂度代价;另一方面通过增强多级矢量量化算法来进一步降低复杂度。在复杂度得到极大降低的同时,仍具有很好的编码性能。     

一种快速的半像素运动矢量搜索算法

提出了一种基于梯度预测的快速半像素运动矢量搜索算法.实验结果表明,在H.263编码器中使用该算法的运算量,比在相同量化阶下的半像素运动矢量搜索算法下降45%,并且图像的PSNR和码率变化很小.该算法可以很容易地应用到H.264的1/4像素运动矢量搜索中.     

宽带ISF参数的转换分类乘积码锥形矢量量化

本文提出了一种新的应用于宽带导抗谱频率参数量化的转换分类乘积码锥形矢量量化方案.该量化器基于转换分类与乘积码锥形矢量量化原理,首先对待量化的ISF参数矢量进行分类,然后按类进行乘积码锥形矢量量化.该算法具有低存储量及低复杂度的特点.实验表明,该算法在每帧编码比特数为46时,平均谱失真比乘积码锥形矢量量化低,且达到了透明量化标准.     

宽带ISF参数的非等系数帧间预测分裂矢量量化方法

 本文提出了一种新的适用于宽带语音编码ISF参数量化的非等系数帧间预测分裂矢量量化方案.该量化方案利用ISF参数的帧间相关性,基于预测分裂矢量量化原理,首先对待量化的ISF参数矢量进行去均值和非等系数帧间预测,然后对去均值后的ISF参数的预测残差进行分裂矢量量化.实验表明,该算法在每帧编码比特数为46bits时达到了透明量化,且平均谱失真比G.722.2中ISF参数量化的平均谱失真小.     

一种基于双正交小流变换和格型矢量量化的视频编码算法

本文提出了基于双正交小流变换和格型矢量量化的视频编码算法，在该方案中，小波变换将图像分解成多分辩率的子带图像，多分辩率运动估值技术实现子带图像的帧间预测，格型徉量量化对预测差值子带图像进行编码，从而获得了性能较好的活动图像编码新算法。     

多级矢量量化法用于图像压缩编码的研究

针对传输凝固图像的要求 ,对多级矢量量化法在图像压缩编码中的应用进行了研究。方案具有压缩比高 ,抗干扰能力强 ,算法简单等特点 ,尤其具有运算量小和复杂度低的突出优点。本文用五幅 2 56× 2 56个像素 ,每像素 8bits的图像进行了计算机模拟实验。实验达到了预期的目的。     

一种随机竞争学习矢量量化图像编码算法

本文分析了确定性模拟退火技术、竞争学习算法在图像编码中的压缩机理,提出了一种新的随机竞争学习矢量量化算法.该算法将竞争过程与代价函数最小化结合起来,在学习过程中引入模拟退火,并针对矢量量化图像编码的特点,提出了新的参数选取策略,具有对初始码书依赖性小,不会局部最小,收敛速度快,码书性能好等优点.文中还通过计算机实践对该方法进行了性能分析,验证了算法的有效性和鲁棒性.     

Multiple shell structured hypercube feature maps for vector quantization of images

A new neural network architecture is proposed for spatial domain image vector quantization (VQ). The proposed model has a multiple shell structure consisting of binary hypercube feature maps of various dimensions, which are extended forms of Kohonen's self-organizing feature maps (SOFMs). It is trained so that each shell can contain similar-feature vectors. A partial search scheme using the neighborhood relationship of hypercube feature maps can reduce the computational complexity drastically with marginal coding efficiency degradation. This feature is especially proper for vector quantization of a large block or high dimension. The proposed scheme can also provide edge preserving VQ by increasing the number of shells, because shells far from the origin are trained to contain edge block features.     

Adaptive edge-based side-match finite-state classified vectorquantization with quadtree map

Vector quantization (VQ) is an effective image coding technique at low bit rate. The side-match finite-state vector quantizer (SMVQ) exploits the correlations between neighboring blocks (vectors) to avoid large gray level transition across block boundaries. A new adaptive edge-based side-match finite-state classified vector quantizer (classified FSVQ) with a quadtree map has been proposed. In classified FSVQ, blocks are arranged into two main classes, edge blocks and nonedge blocks, to avoid selecting a wrong state codebook for an input block. In order to improve the image quality, edge vectors are reclassified into 16 classes. Each class uses a master codebook that is different from the codebooks of other classes. In our experiments, results are given and comparisons are made between the new scheme and ordinary SMVQ and VQ coding techniques. As is shown, the improvement over ordinary SMVQ is up to 1.16 dB at nearly the same bit rate, moreover, the improvement over ordinary VQ can be up to 2.08 dB at the same bit rate for the image, Lena. Further, block boundaries and edge degradation are less visible because of the edge-vector classification. Hence, the perceptual image quality of classified FSVQ is better than that of ordinary SMVQ.     

Image compression using VQ-BTC

Vector quantization (VQ) and block truncation coding (BTC) are successful image compression techniques. However, a reproduced image using VQ or BTC suffers from edge degradation. A new technique that combines the advantages of both VQ and BTC to combat this degradation is presented and is referred to as VQ-BTC. In VQ-BTC, a low-detail block is encoded using VQ. For a high-detail block, a modification of BTC is used to determine the locations of the relatively lighter and relatively darker pixels inside the block and VQ is then used to encode each. VQ-BTC provides improved edge reproduction and much lower bit rates than those obtained by BTC     

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     

Conditional entropy-constrained residual VQ with application toimage coding

This paper introduces an extension of entropy constrained residual vector quantization (VQ) where intervector dependencies are exploited. The method, which we call conditional entropy-constrained residual VQ, employs a high-order entropy conditioning strategy that captures local information in the neighboring vectors. When applied to coding images, the proposed method is shown to achieve better rate-distortion performance than that of entropy-constrained residual vector quantization with less computational complexity and lower memory requirements, moreover, it can be designed to support progressive transmission in a natural way. It is also shown to outperform some of the best predictive and finite-state VQ techniques reported in the literature. This is due partly to the joint optimization between the residual vector quantizer and a high order conditional entropy coder as well as the efficiency of the multistage residual VQ structure and the dynamic nature of the prediction.     

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     

基于视觉误差准则的矢量量化编码

传统矢量量化编码一般采用绝对误差为误差度量准则，并不符合人类视知觉的特性，本文根据人的视觉感知规律，定义了一种可广泛用于图象编码的视觉误差准则，由此导出了一种新的矢量量化编码方法，实验表明，采用这种方法得到的解码图象在视觉效果比传统方法有较大改善。     

DCRVQ: a new strategy for efficient entropy coding ofvector-quantized images

This paper presents a novel predictive coding scheme for image-data compression by vector quantization (VQ). On the basis of a prediction, further compression is achieved by using a dynamic codebook-reordering strategy that allows a more efficient Huffman encoding of vector addresses. The proposed method is lossless, for it increases the compression performances of a baseline vector quantization scheme, without causing any further image degradation. Results are presented and a comparison with Cache-VQ is made     

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.     

Noise reduction of VQ encoded images through anti-gray coding

Noise reduction of VQ encoded images is achieved through the proposed anti-gray coding (AGC) and noise detection and correction scheme. In AGC, binary indices are assigned to the codevector in such a way that the 1-b neighbors of a code vector are as far apart as possible. To detect the channel errors, we first classify an image into uniform and edge regions. Then we propose a mask to detect the channel errors based on the image classification (uniform or edge region) and the characteristics of AGC. We also mathematically derive a criterion for error detection based on the image classification. Once error indices are detected, the recovered indices can be easily chosen from a “candidate set” by minimizing the gray-level transition across the block boundaries in a VQ encoded image. Simulation results show that the proposed technique provides detection results with smaller than 0.1% probability of error and more than 86.3% probability of detection at a random bit error rate of 0.1%, while the undetected errors are invisible. In addition, the proposed detection and correction techniques improve the image quality (compared with that encoded by AGC) by 3.9 dB