基于变换域Wyner-Ziv视频编码的有效重建算法

为了减少分布式视频编码系统重建值与真实值之间误差,提出了基于变换域Wyner-Ziv视频编码最小均方误差(MMSE)重建的一种有效重建算法.该算法充分利用视频帧间相关性,对MMSE重建算法积分区间做出调整,当边信处于解码值对应量化区间之内时,在量化区闻内利用MMSE重建;当边信息处于解码值对应量化区间之外时,对量化区间做出调整,在改进后的区间利用MMSE重建.实验结果表明,与最佳重建最小均方误差重建算法相比,该算法可以有效提高解码视频的平均PSNR.     

一种基于变换域Wyner-Ziv视频编码的有效重建算法

为了减少分布式视频编码系统解码后重建值与真实值之间误差,提出了基于变换域Wyner-Ziv视频编码最小均方误差(MMSE)重建的一种新的有效重建算法。该算法充分利用视频帧间相关性,对MMSE重建算法积分区间做出调整,当边信处于解码值对应量化区间之内时,在量化区间内利用MMSE重建;当边信息处于解码值对应量化区间之外时,对量化区间做出调整,在改进后的区间利用MMSE重建。实验结果表明,与最佳重建最小均方误差重建算法相比,该算法可以有效提高解码视频的平均PSNR。     

一种H.264视频编码码率控制方法

根据视频编码线性率失真函数和对H．264整数变换量化方法分析，提出一种适合于H．264编码算法的码率控制算法。先验证线性率失真函数对H．264编码算法的适用性，再通过将H．264整数变换的量化分解为系数校正和统一量化两个步骤实现了线性率失真函数在H．264码率控制中的应用。实验证明，与JVT—G012码率控制算法相比，码率控制的准确性和信噪比均有所提高。     

消除帧内误差传播的HEVC可逆水印算法

针对帧内实施可逆水印造成误差传播的问题,基于高效视频编码（High Efficiency Video Coding, HEVC）标准,提出一种用于消除帧内误差传播的可逆水印算法。算法充分考虑了HEVC新的编码特性,对帧内嵌入水印后的误差传播情况进行了分析,随后给出了在帧内4×4预测单元中嵌入水印后不会引起误差传播的条件;最后选出满足条件的4×4系数块,采用“和不变”方法将水印自适应地嵌入其量化离散正弦变换系数中。出于减小码率增长的考虑,全0系数块不嵌入水印。实验结果表明,该算法能够有效地消除帧内由嵌入水印引起的误差传播,从而减小视觉失真。同时,算法对码率的影响也较小。      

基于图像特征的视频编码码率控制策略

根据缓存器的状态和信道速率，为待编码帧在图像层上预分配目的编码比特数，并使用小波变换系数来表征图像特性，继而为帧内每一具体宏块选定量化因子，提出了基于图像特征的码率控制策略。仿真结果表明，该码率控制策略能有效地避免缓存器出现上、下溢的情况，并使输出码率趋于稳定。     

适用于ATM网络的信噪比可分级变码率视频编解码方案

提出一种适用于ＡＴＭ网络的两层信噪比分级变码率视频编解码方案。编码的基本层是一个ＭＰＥＧ－２ＭＰＭＬ定码率编码器，增强层对基本层的量化误差进行细量化和变码率编码。     

基于码率预分配的JPEG2000自适应率控制算法

该文提出一种基于码率预分配和反馈控制的自适应率控制算法。算法引入了预测模板,对图像小波变换和量化后EBCOT编码码块的有效比特平面进行独立的熵估计,并以码块估计熵总和指导码率预分配,同时T2编码器由预分配编码比特率自适应地反馈控制编码深度、完成优化截取。实验结果表明,该算法码率分配精确,并有效地减少了编码器计算量和存储量,提高了编码效率,算法复杂度低,易于硬件实现。     

超光谱图像的分布式压缩

针对超光谱图像压缩进行了研究,提出了一种有效的基于分布式信源编码(Distributed Source Coding, DSC)的有损压缩算法。该算法利用多元陪集码和标量量化的方式实现超光谱图像的分布式有损压缩,针对分布式信源编码,利用多波段预测的方式为每个编码块构造边信息,然后采用标量量化的方式对编码块和其边信息同时进行量化处理。根据分布式信源编码原理,给出了各编码块量化后的编码码率。为了减少标量量化带来的信息丢失,算法引入了跳跃策越。部分均方误差意义上损失较大的编码块将由其边信息直接代替。实验结果表明,所提出的算法性能与基于小波变换的算法性能相当;此外,该算法复杂度较低,适合星载超光谱图像的压缩。     

一种面向HEVC的并行码率控制算法

视频通信时通常带宽有限,为了能在规定的目标码率下获得尽可能高质量的解码图像,需要在视频编码时进行码率控制.目前针对HEVC的并行编码以及对应码率控制已成为研究热点,现有并行结构下的平均比特率控制算法受到帧间依赖性的约束,待编码帧无法及时获得与其并行编码帧的实际比特数,因此本文算法通过预测并行帧的实际比特数来进行码率控制,并在此基础提出了自适应调整帧层量化参数补偿值.仿真结果表明,相比已有算法,前者减少码率误差约为3.38％,后者可提高PSNR约为0.204 dB同时减少约0.3％的码率误差.     

基于允许绝对误差指标的静态图像编码算法

提出了一种新的静态图像编码方法，通过将小波域编码和误差的图像域编码相结合，并进行码率优化，使得在满足绝对误差要求的前提下减少总输出码率。同时提出了一种等效误差编码算法，该算法减少了编码的比特平面数，从而进一步降低最终输出码率。     

Resource Allocation of Limited Feedback in Clustered Wireless Mesh Networks

This paper focuses on resource allocation problem of limited feedback in OFDM-based clustered wireless mesh networks. Firstly, the channel quantization codebook is designed according to the equal probability quantizer and joint power, rate and channel quantization threshold codebook scheme (PRCQT) is proposed. Secondly, we deduce the subcarrier allocation criterion utilizing the Lagrange multiplier method. Finally, stochastic resource allocation scheme of limited feedback (SRALF) utilizing the stochastic optimization tools for OFDM-based clustered WMNs is proposed. Simulation results demonstrate SRALF+PRCQT scheme not only improves the network throughput, but also has advantage of limited feedback overhead.     

无线传感器网络中基于多比特量化的极大似然分布式估计方法

在无线传感器网络背景下的分布式估计中,由于传输网络对发送功率和传输带宽的限制,压缩信源冗余、降低通信数据量便成为一个重要的课题.为此,本文提出了一种基于多比特量化观测的分布式估计方法（MQS）,利用渐进性能作为优化准则构造量化阈值优化问题,运用粒子群算法对其进行求解得到最优量化阈值,给出了克拉美罗下界的解析表达式,并与均匀量化方法（UQS）和未量化方法（NQS）进行对比.理论分析和仿真实验表明,MQS的性能优于UQS.当量化深度增大到3时,MQS的估计性能十分接近NQS的估计性能.     

A posteriori quantization of progressive matching pursuit streams

This paper proposes a rate-distortion optimal a posteriori quantization scheme for matching pursuit (MP) coefficients. The a posteriori quantization applies to an MP expansion that has been generated offline and cannot benefit of any feedback loop to the encoder in order to compensate for the quantization noise. The redundancy of the MP dictionary provides an indicator of the relative importance of coefficients and atom indices and, subsequently, on the quantization error. It is used to define a universal upper bound on the decay of the coefficients, sorted in decreasing order of magnitude. A new quantization scheme is then derived, where this bound is used as an Oracle for the design of an optimal a posteriori quantizer. The latter turns the exponentially distributed coefficient entropy-constrained quantization problem into a simple uniform quantization problem. Using simulations with random dictionaries, we show that the proposed exponentially upper bounded quantization (EUQ) clearly outperforms classical schemes. Stepping on the ideal Oracle-based approach, a suboptimal adaptive scheme is then designed that approximates the EUQ but still outperforms competing quantization methods in terms of rate-distortion characteristics. Finally, the proposed quantization method is studied in the context of image coding. It performs similarly to state-of-the-art coding methods (and even better at low rates) while interestingly providing a progressive stream that is very easy to transcode and adapt to changing rate constraints.     

Context modeling based on context quantization with application in wavelet image coding

Context modeling is widely used in image coding to improve the compression performance. However, with no special treatment, the expected compression gain will be cancelled by the model cost introduced by high order context models. Context quantization is an efficient method to deal with this problem. In this paper, we analyze the general context quantization problem in detail and show that context quantization is similar to a common vector quantization problem. If a suitable distortion measure is defined, the optimal context quantizer can be designed by a Lloyd style iterative algorithm. This context quantization strategy is applied to an embedded wavelet coding scheme in which the significance map symbols and sign symbols are directly coded by arithmetic coding with context models designed by the proposed quantization algorithm. Good coding performance is achieved.     

In-loop atom modulus quantization for matching pursuit and its application to video coding

This paper provides a precise analytical study of the selection and modulus quantization of matching pursuit (MP) coefficients. We demonstrate that an optimal rate-distortion trade-off is achieved by selecting the atoms up to a quality-dependent threshold, and by defining the modulus quantizer in terms of that threshold. In doing so, we take into account quantization error re-injection resulting from inserting the modulus quantizer inside the MP atom computation loop. In-loop quantization not only improves coding performance, but also affects the optimal quantizer design for both uniform and nonuniform quantization. We measure the impact of our work in the context of video coding. For both uniform and nonuniform quantization, the precise understanding of the relation between atom selection and quantization results in significant improvements in terms of coding efficiency. At high bitrates, the proposed nonuniform quantization scheme results in 0.5 to 2 dB improvement over the previous method.     

Synthesis filter bank optimization in two-dimensional separablesubband coding systems

Subband coding is a popular and well established technique used in visual communications, such as image and video transmission. In the absence of quantization and transmission errors, the analysis and synthesis filters in a subband coding scheme can be designed to obtain perfect reconstruction of the input signal, but this is no longer the optimal solution in the presence of quantization of the subband coefficients. We presuppose the use of a two-dimensional (2-D) separable subband scheme and we address the problem of designing, for a given analysis filter bank and assuming uniform quantization of the subband coefficients, the set of row and column synthesis filters that minimize the mean squared reconstruction error at the output of the subband system. Since the corresponding optimization problem is inherently nonlinear, we propose a suboptimal solution that extends a one-dimensional (l-D) optimal filter design procedure, already presented in the literature, to a 2-D separable synthesis filter bank. The separable 2-D extension is not trivial, since the processing in one direction, e.g., the rows, alters the statistics of the signals for the design of the filters in the other direction, e.g., the columns. To further simplify the filter design, we propose to model the input image as a 2-D separable Markov process plus an additive white component. Several design examples using both synthetic signals and real world images are presented, showing that the filters designed using the proposed technique can give a significant gain with respect to the perfect reconstruction solution, especially when the dither technique is used for quantization. The simulation results also show that the proposed image model can be conveniently used in the synthesis filter design procedure.     

Minimax deconvolution design of multirate systems with channelnoises: a unified approach

The conventional signal reconstruction problem of multirate systems with channel noises can be cast as a robust multirate deconvolution design problem. We investigate a unified minimax approach for the robust deconvolution design of multirate systems. We discuss two typical multirate systems: the multirate filter bank system and the transmultiplexer system. We consider transmission noises resulting from quantization coding errors or external noises. The deconvolution filters for these systems that we derive are all IIR filters. The keypoint is converting the original robust deconvolution design problem to an equivalent minimax matching problem via polyphase decomposition and noble identities. Then, in spite of the presence of input signals and channel noises, we can solve this minimax matching problem by an optimization technique. The proposed method can be interpreted as designing an optimal multirate deconvolution filter such that the worst-case multirate system reconstruction error is minimized over all possible inputs and noises from the energy perspective. Therefore, our proposed design method is more robust than the conventional design method for multirate systems in the presence of uncertain input signals and channel noises. We present several numerical examples that show the good performance of our design method     

Transmission Subspace Tracking for MIMO Systems With Low-Rate Feedback

This paper describes a feedback algorithm for tracking the dominant subspaces of continuously time-varying channels in multiantenna communication systems. The nature of the problem is quantization of subspaces. It is well known that subspaces can be mathematically modeled as points in a Grassmann manifold. We model the variations between the dominant subspaces of channels at adjacent time instants to be along geodesics in the Grassmann manifold. Instead of quantizing the subspaces themselves, we propose to quantize the geodesic trajectory connecting two subspaces. More specifically, we quantize a key entity that characterizes a geodesic arc: the velocity matrix, which resembles angular speed in a one-dimensional complex space. Two techniques are proposed for quantizing the velocity matrix of the geodesic. In the first, a 1-bit feedback is utilized to indicate the preferred sign of a random velocity matrix of the geodesic. In the other, the velocity matrix is quantized using a Gaussian vector quantization codebook. Numerical results show that the performance of the proposed 1-bit feedback algorithm is better than a previously proposed Grassmannian subspace packing scheme at low-to-medium Doppler frequencies and better than a gradient sign feedback scheme at all Doppler frequencies. In our simulations, the Gaussian vector quantization algorithm is always better than the 1-bit feedback algorithm.     

An Improved Floating-to-Fixed-Point Conversion Scheme for DCT Quantization Algorithm

Conventional fixed-point implementation of the DCT coefficients quantization algorithm in video compression may result in deteriorated image quality. The paper investigates this problem and proposes an improved floating-to-fixed-point conversion scheme. With a proper scaling factor and a new-established look-up table, the proposed fixed-point scheme can obtain bit-wise consistence to the floating-point realization. Experimental results verify the validity of the proposed method.     

图像分块压缩感知观测值的DPCM非均匀量化方法

在实际的信号处理中,有必要对采集到的信号进行量化处理。量化是信号数字化、实现数字信号高效传输的必要步骤。图像分块压缩感知(Block Compressed Sensing, BCS)观测模型中,测量域上图像相邻块的观测值之间存在较强的相关性。根据这一特点,本文应用差分脉冲编码调制(Differential pulse-code modulation, DPCM)系统减小相邻块之间的冗余,并结合非均匀标量量化,对分块压缩感知图像的观测值进行量化处理。文中分析了DPCM系统的预测误差概率分布,发现在统计意义上这一分布与非均匀量化特性的变化趋势具有一致性,并以此作为所提出的量化方法的理论基础。仿真实验表明,本文提出的量化方案有效地提高了压缩感知观测值的量化信噪比(quantized signal to noise ratio, quantized SNR),同时图像的重构质量得到了提升。