可分级无损音频编码技术的新发展

AAZ（Advanced Audio Zip）是一种基于整数变换的无损音频压缩技术，它能提供从有损到无损的可分级编码，且能与MPEG AAC系统兼容。介绍了AAZ的整体技术框架，并对其关键技术——整数改进离散余弦变换、残差映射和位平面编码作了深入分析，最后与其它无损音频压缩技术在性能上进行了比较，结果表明其具有良好的压缩性能。     

一种基于提升小波变换的音频无损编解码方法

提出了一种基于整型提升小波技术的无损音频编解码方法,该方法在AVS（Audio Video Coding Standards）无损编码基线系统提案RM0框架基础上,通过增加整型小波提升模块,对音频信号进行分带处理,增强了带内信号的相关性,提高了信号的压缩比。最后通过与AVS无损编码基线系统RM0的对比,验证了该技术的有效性。     

一种改进型无损音频编码器的MATLAB实现

本文在探讨分帧、预测器、熵编码等设计技术的基础上,阐述了如何利用MATLAB设计、实现和分析无损音频编码器,同时完整地实现了音频信号的编解码。     

可提高编码效率的MPEG-4音频可伸缩无损编码

最近提出的MPEG无损音频编码标准(MPEG-4音频SLS(scalable to Lossless编码技术)提供了一种统一的语音模式,这种语音模式可以将有损语音编码,无损语音编码以及粒度可伸缩语音编码的功能结合在 一个框架中.我们提出了两种提高SLS编码效率的方法,即基于上下关系的算术编码方法和低能量模式编码方法.这两种方法同当前的SLS框架搭配在一起运行,可以保持它的好的特征如:粒度可伸缩性,同时可以成功地改善它的无损压缩性能.     

MPEG-4音频压缩技术的新进展--ALS

在语音信号处理的许多应用中,采用了无损音频压缩的方法.回顾了MPEG-4音频技术的现状,MPEG-4 ALS(Audio Lossless Coding)标准化过程以及编解码技术.详细介绍了编码器中的线性预测、量化、熵编码等模块,展望了MPEG-4 ALS的应用前景.     

MPEG-4 SLS无损增强层的编码

介绍了MPEG-4音频可分级无损压缩编码方案中无损增强层的编码。对无损增强层中的残差映射、比特平面编码、基于上下文的算术编码、低能量编码等技术细节进行了深入讨论和分析。最后将MPEG-4SLS与Monkey’s Audio编码方案进行了比较，以此来说明MPEG-4SLS优异的压缩性能。     

小波变换和预测编码结合的抗差错SAR图像无损压缩算法

分析了即将推出的JPEG2000标准算法和基于自适应的上下文预测编码技术，提出了小波变换有损编码加自适应上下文预测无损残差编码的具有抗差能力的SAR图像无损压缩算法。该算法既具有小波变换图像编码的累进传输，对信道具有一定鲁棒性的特点，而且压缩率高于即将推出的国际标准JPEG2000的无损压缩和国际标准算法JPEG—LS。     

AVS无损音频编码标准——熵编码简介

AVS无损音频编码标准,简称AVS-LS,是AVS工作组制定的用于无损压缩音频数据的新标准。AVS-LS采用线性预测和熵编码来对音频数据进行压缩,其中熵编码器用于对线性预测产生的残差信号进行归一化处理及算术编码。介绍了AVS-LS熵编码器的结构及原理,并对AVS-LS的编码效率进行了测试。测试结果表明：AVS-LS的编码效率略低于MPEG-4 ALS,但高于MPEG-4 SLS和FLAC。     

通信系统中的语音编码技术

语音编解码技术现已成为通信技术的一个重要学科。本文在简要介绍通信系统中语音编码技术的基础上,着重论述了参数编码的残差激励线性预测编码(RELP)算法。这种线性预测编码是一种新的压缩方法,可使码率降低到2.4kb/s以下。     

基于残差图像的ROI编码研究

与JPEG2000中的MAXSHIFT算法不同，本文将ROI编码与零树编码结合，给出一种基于残差图像的ROI编码算法RZ(residue zerotree）。RZ算法基于整数小波变换，对高压缩比下零树编码后ROI区域的残差图像作基于整数平方量化阈值的零树编码，并将所得残差数据熵编码后跟随原编码数据传输，可以实现ROI区域的有损或无损编码。实验结果表明，RZ算法实现了基于嵌入式零对小波变换（EZW）框架较低复杂度的ROI编码，可应用于基于网络的图像传输。     

无损音频编码标准MPEG-4 ALS

介绍了MPEG-4的音频无损压缩标准草案——MPEG-4 ALS,并且与目前常见的一些无损音频编码算法作比较。     

Wavelet-based lossy-to-lossless ECG compression in a unified vector quantization framework

In a prior work, a wavelet-based vector quantization (VQ) approach was proposed to perform lossy compression of electrocardiogram (ECG) signals. In this paper, we investigate and fix its coding inefficiency problem in lossless compression and extend it to allow both lossy and lossless compression in a unified coding framework. The well-known 9/7 filters and 5/3 integer filters are used to implement the wavelet transform (WT) for lossy and lossless compression, respectively. The codebook updating mechanism, originally designed for lossy compression, is modified to allow lossless compression as well. In addition, a new and cost-effective coding strategy is proposed to enhance the coding efficiency of set partitioning in hierarchical tree (SPIHT) at the less significant bit representation of a WT coefficient. ECG records from the MIT/BIH Arrhythmia and European ST-T Databases are selected as test data. In terms of the coding efficiency for lossless compression, experimental results show that the proposed codec improves the direct SPIHT approach and the prior work by about 33% and 26%, respectively.     

A reduced Laplacian pyramid for lossless and progressive imagecommunication

The Laplacian pyramid (LP) is appropriate for lossy image compression; conversely, the reduced-difference pyramid (RDP), having as many data as pixels, gives a better performance with lossless encoding. A reduced LP is designed by discarding the anti-aliasing filter and adopting a half-band interpolator, thus retaining three over four of the LP coefficients. Lossless coding outperforms both LP and RDP, especially when dealing with medical images     

Lossless compression of digital audio

Lossless audio compression is likely to play an important part in music distribution over the Internet, DVD audio, digital audio archiving, and mixing. The article is a survey and a classification of the current state-of-the-art lossless audio compression algorithms. This study finds that lossless audio coders have reached a limit in what can be achieved for lossless compression of audio. It also describes a new lossless audio coder called AudioPak, which low algorithmic complexity and performs well or even better than most of the lossless audio coders that have been described in the literature     

Low noise reversible MDCT (RMDCT) and its application in progressive-to-lossless embedded audio coding

A reversible transform converts an integer input to an integer output, while retaining the ability to reconstruct the exact input from the output sequence. It is one of the key components for lossless and progressive-to-lossless audio codecs. In this work, we investigate the desired characteristics of a high-performance reversible transform. Specifically, we show that the smaller the quantization noise of the reversible modified discrete cosine transform (RMDCT), the better the compression performance of the lossless and progressive-to-lossless codec that utilizes the transform. Armed with this knowledge, we develop a number of RMDCT solutions. The first RMDCT solution is implemented by turning every rotation module of a float MDCT (FMDCT) into a reversible rotation, which uses multiple factorizations to further reduce the quantization noise. The second and third solutions use the matrix lifting to implement a reversible fast Fourier transform (FFT) and a reversible fractional-shifted FFT, respectively, which are further combined with the reversible rotations to form the RMDCT. With the matrix lifting, we can design the RMDCT that has less quantization noise and can still be computed efficiently. A progressive-to-lossless embedded audio codec (PLEAC) employing the RMDCT is implemented with superior results for both lossless and lossy audio compression.     

Rank-order polynomial subband decomposition for medical image compression

In this paper, the problem of progressive lossless image coding is addressed. A nonlinear decomposition for progressive lossless compression is presented. The decomposition into subbands is called rank-order polynomial decomposition (ROPD) according to the polynomial prediction models used. The decomposition method presented here is a further development and generalization of the morphological subband decomposition (MSD) introduced earlier by the same research group. It is shown that ROPD provides similar or slightly better results than the compared coding schemes such as the codec based on set partitioning in hierarchical trees (SPIHT) and the codec based on wavelet/trellis-coded quantization (WTCQ). Our proposed method highly outperforms the standard JPEG. The proposed lossless compression scheme has the functionality of having a completely embedded bit stream, which allows for data browsing. It is shown that the ROPD has a better lossless rate than the MSD but it has also a much better browsing quality when only a part of the bit stream is decompressed. Finally, the possibility of hybrid lossy/lossless compression is presented using ultrasound images. As with other compression algorithms, considerable gain can be obtained if only the regions of interest are compressed losslessly.     

一种改进的嵌入式零树小波编码算法

嵌入式零树小波编码算法是基于小波变换的一种图像压缩方法,它可以实现渐进编解码,进行有损或无损压缩,具有较高的压缩比和图像恢复质量.全文在研究嵌入式零树小波编码算法及原理的基础上,表述了算法的应用过程,阐述了具体的实现思路,指出了其不足之处,并在原来方法的基础上进行了有效改进,具有比较好的效果.