DCT域网格编码矢量量化

本文提出了在离散余弦变换(DCT)域作网格编码矢量量化(TCVQ),并利用量化后相关性的新方法.该方法结合了DCT变换、矢量量化和网格编码量化的优点,它充分利用信号DCT域和时域的相关性来逼近率失真下界,并利用维特比算法来寻找最佳量化序列.同时,它还利用量化后码字间存在的相关性来进一步降低编码率.仿真结果表明,该方法在保持高压缩比的同时具有很高的信噪比性能.在相同编码率下它比全搜索矢量量化(VQ)好1dB左右.同时该方法具有计算量适中,解码简单以及对误差扩散不敏感的优点.     

基于多级零树编码的小波系数网格编码量化

该文提出了对小波图像作多级零树编码后进行网格编码量化（TCQ）的新方法。首先利用子带间的相关性进行零树编码，然后利用卷积编码和信号空间扩展来增大量化信号间的欧氏距离，并用Viterbi算法寻找最优量化序列。仿真结果表明，该方法比零树编码后采用最优量化要提高0．3dB左右。该方法还具有编码计算复杂度适中，解码简单的优点。     

基于DCT-TCQ的SAR原始数据压缩算法

该文提出了一种基于离散余弦变换(DCT)和网格编码量化(TCQ)的SAR原始数据压缩算法。SAR原始数据可以看成是距离向和方位向的2维线性调频信号的线性平移叠加,因而含有丰富的频率分量,对DCT系数按频率分组,根据各组频率分量系数方差确定量化比特分配,然后对量化系数进行TCQ编码量化,可以大幅度提高了量化增益。真实SAR原始数据实验结果表明:该算法使SAR原始数据的压缩性能指标较现有算法有了明显提高。     

二维网格编码矢量量化及其在静止图像量化中的应用

该文提出了在二维码书空间中，在矢量量化（VQ）的基础上，应用网格编码量化（TCQ）的思想来实现量化的新方法－－二维网格编码矢量量化（2D－TCVQ）。该方法首先把小码书扩展成大的虚码书，然后用网格编码矢量量化（TCVQ）的方法在扩大的二维码书空间中用维物比算法来寻找最佳量化路径。码书扩大造成第一子集最小失真减小从提高了量化性能。由于二维TCVQ采用的码书尺寸较小，因而可以应用到低存贮、低功耗的编解码环境。仿真结果表明，同一码书尺寸下，二维TCVQ比TCVQ好0．5dB左右。同时，该方法具有计算量适中，解码简单以及对误差扩散不敏感的优点。     

基于四叉树分类的小波图像二维网络编码量化

本文提出了一种小波图像在进行小波系数四叉树分类后运用二维网格编码量化（2D-TCQ）的新方法。首先根据小波图像中各子带系数的带间相关性对其进行四叉树分类,然后在扩展的二维码书空间对重要类系数进行网格编码量化,运用维特比算法寻找最优量化序列,最终形成有序的嵌入式编码比特流。仿真结果表明,该方法在相同编码率下与SPIHT算法相比,PSNR获得了0.4dB左右的改善;使用小一倍的码书,相同编码率下比四叉树分类后使用一维TCQ获得了0.1dB左右的改善。由于本方法可以采用较小的码书尺寸,计算量较小,所以适用于低存贮、低功耗的编解码环境。     

自适应网格编码量化在图像压缩中的应用

提出了一种对网格编码量化(TCQ)算法进行改进的方法-自适应网格编码量化算法(ADTCQ),并将其应用于JPEG2000图像压缩.自适应网格编码量化算法(ADTCQ)采用了多级TCQ的结构,并且通过利用已处理的数据对网格结构做出自适应调整.实验结果显示,应用于图像压缩时自适应网格编码量化(ADTCQ)算法的性能要明显优于标准TCQ算法.     

结合相干斑抑制的小波域SAR图像网格编码量化

本文结合SAR图像相干斑的抑制,研究了一种在小波域对带噪SAR图像做网格编码量化的新方法。首先将SAR图像在小波域内实施软阀值去噪声,然后根据SAR图像在小波域中各子带系数固有的树结构关系对其进行零树分类,对分类后的重要性小波系数进行网格编码量化,利用卷积编码和信号空间扩展来增大量化信号间的欧氏距离,并用维特比算法寻找最优量化序列。该方法综合了小波相干斑抑制、零树编码、网格编码量化技术,不仅利用了信号小波变换域的空间相关性,而且也较好地利用了信号间的时间相关性。在压缩的同时进行了相干斑抑制,在SAR图像的压缩中取得了很好的效果。     

基于压缩感知的线谱对参数降维量化算法

为实现高质量的极低速语音编码,提出一种基于压缩感知理论的线谱对(LSP)参数降维量化算法。编码端利用压缩感知理论对超帧LSP高维矢量进行降维处理,将原始LSP参数投影到低维空间,得到低维测量值,然后采用分裂矢量量化算法对测量值进行量化;解码端以量化后的测量值为已知条件,利用正交匹配追踪算法重构出原始LSP高维矢量。实验结果表明,本算法相对低速语音编码中的矩阵量化方案,平均谱失真降低了0.23dB,相对基于DCT变换的降维量化方案,平均谱失真降低了0.13dB。这种先降维再量化的思想可以大幅减少编码所需的比特数及码本存储复杂度,有效降低语音编码速率,并且合成语音可懂度、自然度较高,音质虽有所失真,但基本上感觉不到明显的听觉质量下降。      

一种高效的基于矢量量化的彩色视频图像压缩编码方法

针对彩色视频图像提出了一种DCT域内基于矢量量化的高效编码方法.为去掉彩色图像各分量间的相关性,首先将图像由RGB空间转换到YUV空间,然后根据人类视觉特征(HVS)对色度信号U、V进行了亚采样和平均化处理;对亮度信号Y则进行分块DCT变换,并根据HVS特征对变化域内的块矢量进行自适应分类,然后根据矢量的类型分别构造码矢和进行全局码书设计.提出的全局码书设计方案可以根据帧间相关性及码字使用频率,对码书的内容自动进行更新和替换,以适应场景内容的变化.实验结果表明:在保证图像重建质量的前提下,本文提出的方法具有较高的压缩效率,比较适合于视频会议以及水下视频观测等应用场合.     

基于空间组合矢量分类的小波图像网格编码矢量量化

本文基于零树编码、矢量分类和网格编码量化的思想,提出了对小波图像采用空间矢量组合和分类后进行网格编码矢量量化的新方法.该方法充分利用了各高频子带系数频率相关性和空间约束性,依据组合矢量能量和零树矢量综合判定进行分类,整幅图像只需单一量化码书,分类信息占用比特数少.对重要类矢量实行加权网格编码矢量量化,利用卷积编码扩展信号空间以增大量化信号间的欧氏距离,用维特比算法搜索最优量化序列,比使用矢量量化提高了0.6db左右.该方法编码计算复杂度适中,解码简单,可达到很好的压缩效果.     

Compression of hyperspectral imagery using the 3-D DCT and hybridDPCM/DCT

Two systems are presented for compression of hyperspectral imagery which utilize trellis coded quantization (TCQ). Specifically, the first system uses TCQ to encode transform coefficients resulting from the application of an 8×8×8 discrete cosine transform (DCT). The second systems uses DPCM to spectrally decorrelate the data, while a 2D DCT coding scheme is used for spatial decorrelation. Side information and rate allocation strategies are discussed. Entropy-constrained code-books are designed using a modified version of the generalized Lloyd algorithm. These entropy constrained systems achieve compression ratios of greater than 70:1 with average PSNRs of the coded hyperspectral sequences exceeding 40.0 dB     

Joint optimization of TCQ-TCM systems

Signal set expansion and set partitioning ideas from trellis coded modulation (TCM) have been previously applied to develop a source coding technique known as trellis coded quantization (TCQ). It has been also known that joint optimization of source and channel encoders/decoders results in substantial performance improvement. In this work, we investigate the performance of joint TCQ/TCM systems and develop various system optimization techniques. We consider channel optimized TCQ levels and asymmetric TCM symbol sets, and propose a simulated annealing based algorithm for joint optimization of TCQ levels and TCM symbols. Using these techniques signal to quantization noise ratio (SQNR) gains of 0.5-4 dB over nonoptimized systems are attained off-line. This, in turn, provides all the gain of going to a one step higher order trellis     

Trellis-coded quantization designed for noisy channels

Trellis-coded quantization (TCQ) of memoryless sources is developed for transmission over a binary symmetric channel. The optimized TCQ coder can achieve essentially the same performance as Ayanoglu and Gray's (1987) unconstrained trellis coding optimized for the binary symmetric channel, but with a much lower implementation complexity for transmission rates above 1 b/sample. In most cases, the optimized TCQ coder also provides larger signal-to-noise ratio than Farvardin and Vaishampayan's (1991) channel-optimized vector quantization. Algorithms are developed for the joint design of trellis-coded quantization/modulation (TCQ/TCM). The jointly designed TCQ/TCM system outperforms the straightforward cascade of separately designed TCQ and TCM systems. The improvement is most significant at low channel signal-to-noise ratio. For a first-order Gauss-Markov source, the predictive TCQ/TCM performance can exceed that of optimum pulse amplitude modulation     

Wyner-Ziv coding based on TCQ and LDPC codes

This paper considers trellis coded quantization (TCQ) and low-density parity-check (LDPC) codes for the quadratic Gaussian Wyner-Ziv coding problem. After TCQ of the source X, LDPC codes are used to implement Slepian-Wolf coding of the quantized source Q(X) with side information Y at the decoder. Assuming 256-state TCQ and ideal Slepian- Wolf coding in the sense of achieving the theoretical limit H(Q(X)|Y ), we experimentally show that Slepian-Wolf coded TCQ performs 0.2 dB away from the Wyner-Ziv distortionrate function D/sub WZ/R) at high rate. This result mirrors that of entropy-constrained TCQ in classic source coding of Gaussian sources. Furthermore, using 8,192-state TCQ and assuming ideal Slepian-Wolf coding, our simulations show that Slepian-Wolf coded TCQ performs only 0.1 dB away from D/sub WZ/R) at high rate. These results establish the practical performance limit of Slepian-Wolf coded TCQ for quadratic Gaussian Wyner-Ziv coding. Practical designs give performance very close to the theoretical limit. For example, with 8,192-state TCQ, irregular LDPC codes for Slepian-Wolf coding and optimal non-linear estimation at the decoder, our performance gap to DWZ(R) is 0.20 dB, 0.22 dB, 0.30 dB, and 0.93 dB at 3.83 bit per sample (b/s), 1.83 b/s, 1.53 b/s, and 1.05 b/s, respectively. When 256-state 4-D trellis-coded vector quantization instead of TCQ is employed, the performance gap to DWZ(R) is 0.51 dB, 0.51 dB, 0.54 dB, and 0.80 dB at 2.04 b/s, 1.38 b/s, 1.0 b/s, and 0.5 b/s, respectively.