视觉加权的率失真优化死区调整算法

提出了一种基于人眼视觉模型加权的率失真优化自适应调整量化器死区算法。该算法应用率失真优化技术自适应地调整量化器死区的大小,提高量化器的编码性能,改进后的量化器在高码率下增益可以达到1dB以上。通过将人眼视觉模型引入率失真代价值的计算,进一步提高了量化器的主观性能。在H.264上的软件模拟结果表明,该算法不但能有效提高视频的主观视觉质量,而且在相同主观质量条件下平均能够节约10%的码率。     

用增益精确值和归一化波形码书改进G.728

分析研究了增益、波形乘积码书结构的缺陷，设计了归一化波形码书和精确表示增益的LD-CELP方案。采用自适应预测和自适应量化对增益的精确值进行量化，在3bit和4bit自适应量化时比G．728固定量化增益分别提高0．5dB和6dB。采用4bit自适应量化和64波形码书比G.728SNR提高约1dB。将G．728综合滤波器由50阶减少到30阶，信噪比不变而算法复杂性降低约20%。     

基于率失真优化的递进UTCQ编码

本文提出了一种基于UTCQ量化器的递进静态图像小波编码算法。一致网格编码量化(UTCQ)用于小波系数的量化并得到了非常好的量化效果。UTCQ超集索引值构成系数位平面,率失真优化按照率失真斜率递减的顺序从系数位平面选择编码系数位。最先编码的位具有最大的率失真斜率,每编码一位都会使失真减少最大。率失真斜率的计算仅仅是利用MQ自适应算术编码器的概率状态估计表而进行的查表过程。MQ算术编码器进一步压缩率失真优化选择的系数位。率失真门限方法的编码速度比搜索最大的率失真斜率更快。该算法有较快的编码速度以及好的压缩效果。     

自适应预失真射频功率放大器的多方向搜索优化算法

通常功率放大器的特性会随环境温度、供电电压等多种因素发生变化,为了保证预失真功率放大器的稳定工作,预失真系统的自适应性能就显得非常重要.本文提出了一种新的高效射频预失真功率放大器自适应优化算法,其核心思想是提取放大器输出信号的带外分量作为系统自适应的优化目标,结合具有稳定收敛性的多方向搜索优化算法对预失真系统进行优化调整,使系统始终处于最优工作状态.仿真结果表明运用该算法的预失真系统自适应收敛速度比传统自适应优化算法有明显提高,带外分量可以获得大约15dB的改善.     

基于JPEG双量化特性的彩色伪造图像盲检测

现有的盲检测方法主要针对灰度图像和未压缩图像,很多算法都不能有效地检测彩色压缩伪造图像。本文提出了一种利用JPEG双量化失真特性实现彩色压缩伪造图像盲检测的方法。通过分析伪造图像的制作过程,可知由多幅JPEG图像拼接成的高质量彩色伪造图像中篡改区域和背景区域经历的双量化过程不同。根据这一特性,本文首先使用背景区域的初始量化表估计值对待检测图像进行再压缩处理,定义再压缩后图像各颜色分量的失真函数;然后根据各失真函数在图像不同区域的取值,由各颜色分量分别确定篡改区域;最后综合彩色图像各颜色分量的检测结果,最终识别出彩色图像篡改区域的位置和大小。仿真结果表明该方法不但可以有效地识别彩色伪造图像的篡改区域,而且比基于单一颜色分量的检测方法更加准确。      

基于自适应量化矩阵的硬判决量化算法

在视频编码中,视频量化一般分为硬判决量化(HDQ)和软判决量化(SDQ),HDQ与SDQ相比,编码性能虽有所损失,但其编码复杂度低,易于硬件实现的优点依旧是主流编码器所主要采用的量化算法.人眼具有对图像中的高频细节不敏感的特性.因此,基于Bayes最小误判概率约束,离线构建基于视频内容自适应的量化矩阵,在模拟感知SDQ算法机理下,对高频低频分量采用不同的量化步长,提高视频的主观质量和HDQ算法性能.仿真实验表明,相比于传统的HDQ算法,该文算法能达到平均5.048％的码率节省,其中WVGA和WQVGA格式平均达到10.65％的码率节省.相比于感知SDQ算法,平均码率增加仅有1.464％;算法复杂度方面,编码一帧的时间相比于感知SDQ节省了32.956％.     

自适应帧内更新和码率控制的联合优化

帧内更新是在视频编码中常采用的容错手段,它能够有效的阻止差错传播。帧内更新率高,能够有效的抑制差错扩散,但是会降低编码效率,在码率一定时,会增大信源失真。只有选择合适的更新率,才能更好的达到抗差错的目的。本文提出了一种基于端到端模型,对自适应更新和码率控制进行联合优化的算法,在满足码率的要求下,自适应的调整帧内更新位置和更新数量,实现端到端失真最小。实验结果表明,本文提出的算法与随机更新算法相比,PSNR提高了0．5～1．5dB。     

MPEG－2视频编码的自适应量化器设计

本文在研究ＭＰＥＧ－２ＴＭ５建议的自适应量化策略的基础上，设计了一种新的自适应量化器。以块为基础分析宏块的局部视觉活动特性，并通过综合评价宏块中各块的视觉活动特性来最终决定自适应视觉量化因子。实验结果表明，本文所设计的自适应量化器能均匀分布图像编码主观失真，改善了图像质量，特别是减少了平坦区的块效应，降低了平坦区强边缘的失真。     

宽带系统中有记忆大功率功放的数字预失真器研究

针对高峰均比的宽带输入信号,提出了一套联合峰均比抑制技术和基带自适应预失真技术的数字预失真器设计方案,并仿真了峰值抵消算法和自适应预失真算法.结果表明对于峰均比为8.4 dB的输入信号,经过1.5 dB的削峰处理后,预失真器改善带外频谱抑制27 dB,非常有效地补偿了功放的非线性失真,提高了功放效率,对发射机功放线性化技术有一定的实用价值.     

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。     

Adaptive scalar quantization without side information

In this paper, we introduce a novel technique for adaptive scalar quantization. Adaptivity is useful in applications, including image compression, where the statistics of the source are either not known a priori or will change over time. Our algorithm uses previously quantized samples to estimate the distribution of the source, and does not require that side information be sent in order to adapt to changing source statistics. Our quantization scheme is thus backward adaptive. We propose that an adaptive quantizer can be separated into two building blocks, namely, model estimation and quantizer design. The model estimation produces an estimate of the changing source probability density function, which is then used to redesign the quantizer using standard techniques. We introduce nonparametric estimation techniques that only assume smoothness of the input distribution. We discuss the various sources of error in our estimation and argue that, for a wide class of sources with a smooth probability density function (pdf), we provide a good approximation to a "universal" quantizer, with the approximation becoming better as the rate increases. We study the performance of our scheme and show how the loss due to adaptivity is minimal in typical scenarios. In particular, we provide examples and show how our technique can achieve signal-to-noise ratios within 0.05 dB of the optimal Lloyd-Max quantizer for a memoryless source, while achieving over 1.5 dB gain over a fixed quantizer for a bimodal source.     

Quantization based on a novel sample-adaptive product quantizer(SAPQ)

In this paper, we propose a novel feedforward adaptive quantization scheme called the sample-adaptive product quantizer (SAPQ). This is a structurally constrained vector quantizer that uses unions of product codebooks. SAPQ is based on a concept of adaptive quantization to the varying samples of the source and is very different from traditional adaptation techniques for nonstationary sources. SAPQ quantizes each source sample using a sequence of quantizers. Even when using scalar quantization in SAPQ, we can achieve performance comparable to vector quantization (with the complexity still close to that of scalar quantization). We also show that important lattice-based vector quantizers can be constructed using scalar quantization in SAPQ. We mathematically analyze SAPQ and propose a algorithm to implement it. We numerically study SAPQ for independent and identically distributed Gaussian and Laplacian sources. Through our numerical study, we find that SAPQ using scalar quantizers achieves typical gains of 13 dB in distortion over the Lloyd-Max quantizer. We also show that SAPQ can he used in conjunction with vector quantizers to further improve the gains     

Encoding of Speech Spectral Parameters Using Adaptive Quantization Range Method

Efficient quantization methods of the line spectrum pairs (LSP) which have good performances, low complexity and memory are proposed. The adaptive quantization range method utilizing the ordering property of LSP parameters is used in a scalar quantizer and a vector‐scalar hybrid quantizer. As the maximum quantization range of each LSP parameter is varied adaptively on the quantized value of the previous order's LSP parameter, efficient quantization methods can be obtained. The proposed scalar quantization algorithm needs 31 bits/frame, which is 3 bits less per frame than in the conventional scalar quantization method with interframe prediction to maintain the transparent quality of speech. The improved vector‐scalar quantizer achieves an average spectral distortion of 1 dB using 26 bits/frame. The performances of proposed quantization methods are also evaluated in the transmission errors.     

Design of sample adaptive product quantizers for noisy channels

Channel-optimized vector quantization (COVQ) has proven to be an effective joint source-channel coding technique that makes the underlying quantizer robust to channel noise. Unfortunately, COVQ retains the high encoding complexity of the standard vector quantizer (VQ) for medium-to-high quantization dimensions and moderate-to-good channel conditions. A technique called sample adaptive product quantization (SAPQ) was recently introduced by Kim and Shroff to reduce the complexity of the VQ while achieving comparable distortions. In this letter, we generalize the design of SAPQ for the case of memoryless noisy channels by optimizing the quantizer with respect to both source and channel statistics. Numerical results demonstrate that the channel-optimized SAPQ (COSAPQ) achieves comparable performance to the COVQ (within 0.2 dB), while maintaining considerably lower encoding complexity (up to half of that of COVQ) and storage requirements. Robustness of the COSAPQ system against channel mismatch is also examined.     

On the design of an optimal quantizer

The problem of designing an optimal quantizer with a fixed number of levels for a wide class of error weighting functions and an arbitrary distribution function is discussed. The existence of an optimal quantizer is proved, and a two-stage algorithm for its design is suggested. In this algorithm, at the first stage, Lloyd's iterative Method I is applied for reducing the region where, at the second stage, the search for an optimal quantizer is performed using a hybrid of the dynamic programming algorithm and the Lloyd-Max algorithm, which achieves the absolute optimality of dynamic programming with much less computational effort. For a continuous distribution with log-concave density and an increasing convex weighting function of the absolute quantization error, a reliable method is presented to compute the parameters of the optimal quantizer with a known precision using a generalization either of Lloyd's Method I or of the Lloyd-Max algorithm     

Design of Semi-Uniform Quantizers and Their Application in Sigma Delta A/D Converters

In this paper a new type of non-uniform quantizer, semi-uniform quantizer, is introduced. A k-bit semi-uniform quantizer uses the thresholds defined by a (k + 1)-bit uniform quantizer and arranges them in such a way that small-amplitude inputs will be quantized by small quantization steps and large-amplitude inputs by large quantization steps. Therefore the total quantization error power could be reduced and the modulator's dynamic range could be increased by 1-bit. The condition for a semi-uniform quantizer to achieve a better performance than a uniform quantizer is analyzed and verified using a second order 3-bit sigma delta modulator prototype chip, fabricated in 0.35 μm CMOS process. At 32× oversampling ratio the modulator achieves 81 dB dynamic range and 63.8 dB peak SNDR with 3-bit semi-uniform quantizer. With 3-bit uniform quantizer the dynamic range is 70 dB and the peak SNDR is 54.1 dB.     

Gaussian source coding with spherical codes

A fixed-rate shape-gain quantizer for the memoryless Gaussian source is proposed. The shape quantizer is constructed from wrapped spherical codes that map a sphere packing in ℝ^k-1 onto a sphere in ℝ^k, and the gain codebook is a globally optimal scalar quantizer. A wrapped Leech lattice shape quantizer is used to demonstrate a signal-to-quantization-noise ratio within 1 dB of the distortion-rate function for rates above 1 bit per sample, and an improvement over existing techniques of similar complexity. An asymptotic analysis of the tradeoff between gain quantization and shape quantization is also given     

Variable-length constrained-storage tree-structured vectorquantization

Constrained storage vector quantization, (CSVQ), introduced by Chan and Gersho (1990, 1991) allows for the stagewise design of balanced tree-structured residual vector quantization codebooks with low encoding and storage complexities. On the other hand, it has been established by Makhoul et al. (1985), Riskin et al. (1991), and by Mahesh et al. (see IEEE Trans. Inform. Theory, vol.41, p.917-30, 1995) that variable-length tree-structured vector quantizer (VLTSVQ) yields better coding performance than a balanced tree-structured vector quantizer and may even outperform a full-search vector quantizer due to the nonuniform distribution of rate among the subsets of its input space. The variable-length constrained storage tree-structured vector quantization (VLCS-TSVQ) algorithm presented in this paper utilizes the codebook sharing by multiple vector sources concept as in CSVQ to greedily grow an unbalanced tree structured residual vector quantizer with constrained storage. It is demonstrated by simulations on test sets from various synthetic one dimensional (1-D) sources and real-world images that the performance of VLCS-TSVQ, whose codebook storage complexity varies linearly with rate, can come very close to the performance of greedy growth VLTSVQ of Riskin et al. and Mahesh et al. The dramatically reduced size of the overall codebook allows the transmission of the code vector probabilities as side information for source adaptive entropy coding.     

基于有记忆信源模型的视频编码量化算法研究

针对视音频优化量化算法研究,本文通过模拟最佳软判决量化特点,引入系数间的相关性,在硬判决量化基础上提出一种有记忆信源模型的量化算法.该模型统计了量化块中每个位置编码比特节省估计量,利用贝叶斯二值判别法计算出可区分量化结果的最佳估计阈值,二值做差得到码率节省余量,利用码率节省余量实现对量化偏移量的动态调节,从而优化量化算法.实验表明,基于本文的有记忆信源模型相较于传统硬判决量化有显著性能提升,BD-PSNR有0.0964dB提升,相当于3.5723％码率节省.本文偏移量模型基于离线建模,实时计算所需额外计算复杂度较小,适合硬件编码器架构设计实现.     

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.