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。     

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．     

序号预测矢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。     

静态图像自适应编码的一种实现方法

在静态图像的方块截断编码中由于受到每个子块中电平级数的限制,编码过程无法正确识别图像边缘的变化,在一些灰度变化比较剧烈的子块中很容易造成有用信息的丢失。为了解决以上问题,本文提出了一种自适应方块截断编码算法,仿真结果表明:本文的自适应算法对图像边缘的恢复有较大的改进,视觉质量有很大的提高。     

一种采用非均匀量化的近似Log-MAP算法

为了简化Log-MAP算法、减小其硬件查表实现时所占用的外存储单元,采用了非均匀量化的方法分别对Log-MAP算法中的校正函数进行两电平和五电平近似,从而得到相应的量化函数。并将该量化函数用于LTE Turbo译码器,在AWGN信道上进行软迭代译码仿真。仿真结果表明,两电平和五电平非均匀量化的近似Log-MAP算法将Max-Log-MAP算法的译码性能提高了0.2～0.3 dB,与原Log-MAP算法性能接近,但却能大大减少查表时需要检索的数据量。     

一种基于BTC-PCA的彩色图象压缩方法

本文提出的彩色图象压缩方法基于BTC（块截断编码）和PCA（主成分分析）的有效结合。先利用PCA方法将图象象素分成二类，简单地实现了矢量量化；然后用BTC方法对分类象素进行简单而高效的编码。实施结果表明此方法简单、易行，效果良好。     

低通图象的自适应混合矢量量化编码

本文提出了一种基于图象分解与自适应混合矢量量化的静态图象高效编码方案,详细介绍了低通图象的混合矢量量化编码方法。就低频分量而言,在80∶1的压缩比时可以获得较好的译码图象质量。     

一种基于JBIG的静止图象编码方案

本文从空域量化的角度．提出了一种基于JBIG的“AMBTC＋JBIG”静止图象编码方案．自适应多级块截断量化器”AMBTC”（AdaptiveMultilevelBlockTruncationCoder）的引入提高了灰度保持特性，“JBIG”保证了高效的编码特性，从算法的整体性能上，本混合算法至少达到了JPEG标准的水平，而在边界保持性方面，则明显优于JPEG质量．     

基于低密度校验码的OFDM编码调制译码算法

低密度校验码(LDPC)具有编码增益高、译码速度快、性能接近Shannon限的优点。LDPC码应用于OFDM，能有效地提高多径环境下OFDM的BER性能。本文首先简单介绍LDPC码及其概率域上的译码算法，在此基础上对译码算法作融合，阐述概率似然比的译码算法。为了把LDPC应用于OFDM系统上，提出了多电平调制下的LDPC译码的算法。仿真结果表明，在AWGN和Rayleigh信道下，此算法正确有效。     

自适应多值量化的亚抽样块截断编码

本文报道了一种新的活动图像编码算法,称为自适应多值量化的亚抽样块截断编码。模拟结果表明,这种新算法比现有的其它同类算法有更好的性能,压缩比为３０,信噪比为３５．４ｄＢ,图像主观质量良好,在现有个人计算机上可以用软件实时实现。     

New bit rate reduction techniques for block truncation coding

Two simple and fast block truncation coding algorithms which require reduced bit rates are presented in this paper. The proposed algorithms are compared with the modified block truncation coding technique (MBTC) with regard to the mean square error (MSE), bit rates and subjective quality of the reconstructed images. It is found that the results obtained with the proposed algorithms at reduced bit rates are comparable to those obtained with the MBTC. Further, it is shown that one of the proposed algorithms yields results which are comparable to those achieved with more complex hybrid algorithms     

A novel block truncation coding of color images using aquaternion-moment-preserving principle

Block truncation coding (BTC) is an efficient tool for image compression. To compress color-pixel blocks, a novel color BTC algorithm, called quaternion-moment block truncation coding (QMBTC), is presented. Analytical formulas for QMBTC, whose computation time is on the order of pixel block size, are derived by using quaternion arithmetic and the moment-preserving principle. The proposed color BTC algorithm can adaptively truncate a pixel block into one or two output classes according to the distribution of the color values inside the blocks. The experimental results show that the compression ratio is increased as compared with existing color BTC algorithms, and the picture quality of the reconstructed images is satisfactory. In addition, a post-BTC data compression scheme is proposed to further compress the subimage constructed by reproduction colors of truncated pixel blocks. Using a lookup table to display decoded data, this postprocessing scheme can output images acceptable to human eyes     

Fundamental error analysis and geometric interpretation for block truncation coding techniques

Various variants and hybrid approaches evolved from the origin of Delp and Mitchell's block truncation coding (BTC) or moment preserving quantizer (MPQ) have formed a niche as an effective and simple image compression methodology with attractive coding performance achieved at moderate bitrates. As BTC is still lacking a fundamental error analysis, in this paper we present some fundamental insights regarding one-bit (or two-level) BTC's truncation error by providing mathematical analysis as well as novel geometric interpretation. We further show that the mean-square error (MSE) of Lema and Mitchell's absolute moment block truncation coding (AMBTC) is always bounded below (i.e., less than or equal to) that of BTC. Therefore, with additional advantages in computation and implementation, AMBTC is always superior. Furthermore, we developed a new adaptive equal sign position optimization (ESPO) algorithm for optimum pixel classification. Our quantization error analysis shows that incorporating the ESPO algorithm into conventional AMBTC or BTC achieves minimum MSE in either case.     

基于方块编码的图像纹理特征提取及检索算法

针对灰度共生矩阵（GLCM）在提取纹理特征时存在的问题,提出一种基于方块编码（BTC）的图像纹理特征的检索算法。首先将图像分成互不重叠的子图像块,然后利用BTC的思想对这些图像块进行编码,进而定义图像的纹理基元并以此作为对图像的纹理描述,并提出采用一种改进的基于纹理基元的共生矩阵来获取纹理特征。实验结果表明,该方法既有效地利用了图像的纹理信息,又考虑了图像的空间和形状信息,具有较好的检索效果。     

Block truncation coding using Hopfield neural network

The authors extend the analysis of the block truncation coding (BTC) algorithm using a Hopfield neural network (HNN). They show that its performance is suboptimum (in the mean square error sense) and that alternative (non-neural network) BTC algorithms are available with virtually the same performance.<>     

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     

Modified algorithm for block truncation coding of monochrome images

Block truncation coding (BTC) is a recent technique used in the coding of image data. In the letter a modified technique for BTC coding of image data is presented which is algorithmically simple and hence easy to implement. This new technique uses only the first-order statistical information as `block overhead?. The new algorithm is shown to be optimum in the mean-square sense for a particular class of BTC algorithms. The letter presents the results of using the new algorithm for a typical image and compares the performance with that of the earlier algorithm for the same image.     

Two interpolative BTC image coding schemes

Two interpolative BTC (block truncation coding) image coding schemes with optimal stack filters (in the minimum MAE sense) as the interpolator are suggested. Compared to the standard BTC, the new schemes yield >25% and >37.5% reductions in bit rate, respectively, with little performance degradation. Compared to a previous interpolative BTC coding method, the quality of coded images has been greatly improved with the new schemes (at the same or even a lower rate).<>     

Improved block truncation coding using modified error diffusion

Block truncation coding (BTC) is an efficient compression technique, offering good image quality. Nonetheless, the blocking effect inherent in BTC causes severe perceptual artefact when compression ratio is increased. Conversely, error diffusion (EDF) enjoys the benefit of diffusing the quantised error into neighbouring pixels. Consequently, the average tones in any local areas of the error-diffused image are preserved unchanged. Presented is a hybrid approach which combines the proposed modified EDF with BTC. As documented in experimental results, image quality is much better than BTC, and the complexity is even lower than traditional BTC.