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。     

移动环境视频编码功率-码率-失真模型研究

在前期建立视频编码动态功耗模型的基础上,将功率因子引入到经典率失真理论,并对经典码率模型及失真模型进行修正,进而建立视频编码功率-码率-失真模型.实验结果表明,该模型具有较高的精确性,为实现功耗约束条件下视频质量、带宽、功耗达到动态平衡的最优提供理论指导.     

基于Weibull率失真简化模型的帧比特分配算法

为在确保视频压缩码流平稳且带宽受限的情况下提升重建视频质量,本文针对使用Weibull密度分布对压缩视频序列率失真性能分析的方法,对其模型进行数学简化,并以帧层编码速率控制的缓冲器充盈度和重建视频质量为目标,提出了一种新的H.264压缩视频速率控制方法——帧比特分配算法。该算法以H.264的JM16.1为视频压缩编码平台,将简化率失真模型嵌入速率控制算法中,以一个GOP为控制单元对其内部各图像帧进行比特分配与调整,同时以帧为周期进行算法模型参数更新。实验结果表明,本文提出的算法要比JVT-H017的算法在重建视频客观质量（PSNR）方面平均有0.30dB的改善,并能够得到更加平稳质量的视频输出。在实际输出码率方面,本文算法比JVT-H017的算法更低,并且编码缓冲器充盈度较JVT-H017平稳且没有上下溢的情况出现。      

基于统计建模的HEVC快速率失真估计算法

针对高效视频编码(HEVC)率失真优化过程中复杂的码率与失真计算,提出一种高准确性的快速率失真估计模型。该算法评估熵编码中不同语法元素的贡献百分比,并逐个开发语法元素的码率模型以构建整个TU级模型。首先,对系数进行自适应加权,以区分不同系数对码率的不均匀贡献。然后,定义位置参数以描述一个块内非零系数可能的分布模式。通过统计方式对大量样本的模型参数进行微调,得出最终线性码率模型。最后,建立了变换域失真模型来省略不必要的重构过程。实验结果表明,所提算法在RA配置下,可以实现49.76%的复杂度降低,而BD-BR仅增加1.74%。     

基于频域的MPEG-2码率转换技术及其码率控制策略

本文针对MPEG-2三种不同的预测方式,讨论了由参考帧中各块的DCT系数直接求得匹配块的DCT系数的方法,提出了频域内运动补偿的概念和基于频域的无偏视频码率转换技术.其码率控制策略使用了率失真优化求得各编码帧最佳的量化参数,并在G-BFOS算法的基础上对率失真优化的快速算法进行了进一步简化,在保证解的精度的前提下大大降低了计算复杂度.     

基于量化系数均方误差准则的运动估计算法

提出了一种基于量化系数均方误差匹配准则的DCT域运动估计视频编码算法.算法中采用了一种新的运动估计匹配准则,该准则在DCT域内计算逆量化的残差均方误差值.由于该准则已考虑到量化噪声对运动残差能量的影响,因此与传统编码算法相比较,在图像质量基本不变的前提下码率更低.仿真结果显示,基于量化系数均方误差准则的DCT域运动估计算法具有较高的编码效率.     

视频编码线性码率控制模型

为提高视频编码码率控制的性能,通过理论推导及实验验证提出了一种新的视频编码 码率控制模型。通过该模型,编码器可以直接确定量化参数(Quantization Parameter, QP) ,而不必计算量化步长(Quantization Step, Qstep),进而避免了由Qstep 确定QP 过程中的舍入误差,提高了码率控制的准确性。实验结果表明,与H.264/ AVC采用的码率控制方法相比,提出的模型可使跳帧发生的概率更低,恢复视频的质量更稳 定,实际码率与目标码率之间的偏差更小,同时,还能获得更高的峰值信噪比。     

基于ρ域线性率失真模型的H.264帧级码率控制算法1

本文基于ρ域线性率失真模型提出一种适用于H.264的帧级码率控制算法.先验证线性率失真函数对H.264编码算法的适用性,再通过使用混合自适应估计方法精确地估计出该模型惟一的参数θ,实现线性率失真函数在H.264码率控制中的应用.仿真结果表明,与JVT-H017算法相比,新算法不仅获得更精确的码率控制,而且获得更平稳的输出码率和PSNR,同时实现简单,适用于实时视频通信.     

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。     

基于失真-量化模型的视频编码算法优化

失真-量化(D-Q)在视频编码率失真模型中起着关键作用,对主流D-Q模型进行了梳理,对这些模型的准确度和复杂度进行了详细对比,并分析它们各自适用的场合。通过模型验证,发现基于柯西分布的D-Q模型复杂度最低,但是准确度跟其他模型相比略低,就此问题对基于柯西分布的D-Q模型做了改进,以提高模型的精确度,使其更好地应用在率失真优化模式选择和码率控制的算法优化中。     

基于广义高斯分布建模帧级变换系数的VVC编码失真预测模型

通用视频编码（versatile video coding,VVC）采用多种高级编码工具共同实现卓越的编码性能。与高效视频编码（high efficient video coding,HEVC）相比,VVC的变换系数分布（transform coefficient distribution,TCD）具有更尖锐的峰值。针对这一现象,对帧级TCD进行概率密度函数（probability density function,PDF）建模,并提出一种基于统计建模的帧级编码失真预测模型,将帧级失真建模为TCD分布参数和量化参数的函数。实验结果表明,相比于拉普拉斯分布以及柯西分布,广义高斯分布在TCD概率密度拟合方面表现最佳;基于广义高斯分布的失真预测模型的预测结果最接近实际编码失真。     

Study on the distribution of DCT residues and its application to R-D analysis of video coding

Quantization errors in discrete-cosine-transform (DCT) video compression are known as DCT residues. Knowledge on their distribution is essential in understanding rate-distortion (R-D) behaviors of generic video coding. Traditional R-D analysis adopted a simplified distortion model. Those distortion models took only quantization parameter into account. They lack adaptability to variation of video sources, as the distribution of coding errors also depends on the statistics of video source. Another common approach models the distribution of DCT residues by fitting experimental data from coded pictures to conjectured statistical distributions, but it did not provide insights into what gives rise to the distribution of DCT residues. This paper intends to quantify the distribution of DCT residues with respect to video source and with respect to the quantization strategy by understanding the quantization of DCT frequency components. Moreover, it is applied to derive an R-D model to show the advantage of the proposed distribution model.     

LMM-based frame-level rate control for H.264/AVC high-definition video coding

Accurate distribution modeling for the DCT coefficients is greatly important for us to analyze the rate–distortion (R–D) behavior of video encoders. From the experiment, we observed that most of the existing models, paying more attention to the standard-definition (SD) videos, tend not to work well for high-definition (HD) videos. Motivated by this, in this paper, we address the statistical characteristics of DCT coefficients of HD videos coded by H.264/AVC. The contributions of this paper are threefold: first, Laplacian Mixture Model (LMM) is proposed to model the residues instead of using Laplacian or Cauchy distribution; second, the LMM-based analytic rate and distortion models are derived; third, building on the proposed rate and distortion models, a frame-level rate control algorithm is developed. Experimental results show that the proposed rate control method achieves a PSNR improvement of up to 0.85 dB compared with the rate control scheme adopted in the H.264 reference software [1]. Apart from the average visual quality improvement, the temporal visual quality fluctuation is reduced by 17%.     

A Motion Picture Coding Algorithm Using Adaptive DCT Encoding Based on Coefficient Power Distribution Classification

A motion picture coding algorithm using motion-compensated interframe prediction and the adaptive discrete cosine transform (DCT) encoding technique is proposed. High coding efficiency is obtained by the adaptive DCT encoding technique in which encoding parameters are fitted to widely varying characteristics of the interframe differential signal. Segmented DCT subblocks of interframe prediction error are classified into categories based on their coefficient power distribution characteristics. The adaptation gain results from using a suitable variable word length code set designated by the above classification for encoding each quantization index of DCT coefficients. In addition, a new coding parameter control method is introduced based on the information rate estimation of the current frame. This classification promotes high stability because good estimation accuracy of bits consumption for each DCT subblock is obtained by utilizing the category indexes. Simulation results show that the proposed algorithm has enough coding efficiency to transmit videoconferencing motion pictures through a 384 kbit/s channel.     

The quantized DCT and its application to DCT-based video coding

The two-dimensional (2-D) discrete cosine transform (DCT) and the subsequent quantization of the transform coefficients are two computationally demanding steps of any DCT-based video encoder. In this paper, we propose an efficient joint implementation of these two steps, where the precision in computing the DCT can be exchanged for a reduction in the computational complexity. First, the quantization is embedded in the DCT, thus eliminating the need to explicitly quantize the transform coefficients. A multiplierless integer implementation of the quantized DCT (QDCT) is then proposed that performs shift and add operations instead of full multiplications. A sequence of multiplierless QDCT algorithms is obtained with increasing precision and number of computations. Finally, further savings in computations are obtained by terminating the DCT computations whenever intermediate results indicate that the transform and quantization steps will likely result in a block of zero values. The proposed algorithms are applied to, and results are presented for, high-quality MPEG-2 and low bit rate H.263 video encoding.     

HEVC整数DCT变换与量化的FPGA实现

HEVC编码框架采用了比H.264/AVC面积更大的DCT变换和更为灵活的自适应量化,在提高数据处理速度的同时,降低了编解码的失真率.基于HEVC的变换量化原理和模块化的思想,采用并行流水线结构和无乘法器方案实现了整数DCT变换及量化部分.系统采用MODELSIM进行功能仿真,基于Altera公司的Cyclone Ⅱ系列可编程逻辑器件进行硬件验证测试,其最大时钟频率在170 MHz以上,数据处理能力在2 824 Mpixel/s以上,满足HEVC编码标准的性能要求,为HEVC编解码标准的硬件实现提供了参考.     

一种改进的基于柯西模型的H.264码率控制方法

H.264/AVC编码中的码率控制是通过有效控制输出码流的码率来提高其压缩视频质量的重要技术。本文基于H.264/AVC中的JVT-H017码率控制方案提出了一种改进算法。新算法根据H.264中DCT系数的分布特征,将柯西分布引入到码率控制模块,用更精确的柯西率失真模型取代了原先的二次率失真模型。在此基础上,进一步引入了一种联合PSNR比率和MAD比率进行图像复杂度预测的方法,并依此来调整帧级比特的分配和量化参数,克服了在出现复杂运动或场景切换时,因视频序列相邻帧之间相关性降低而导致的MAD预测失准的情况。实验结果表明,与JVT-H017方案及文献[5]中的算法比较,新的算法不仅具有更精确的码率控制,而且明显改善了输出码率的平稳性及重建图像的PSNR。      

Postprocessing of low bit-rate block DCT coded images based on a fields of experts prior.

Transform coding using the discrete cosine transform (DCT) has been widely used in image and video coding standards, but at low bit rates, the coded images suffer from severe visual distortions which prevent further bit reduction. Postprocessing can reduce these distortions and alleviate the conflict between bit rate reduction and quality preservation. Viewing postprocessing as an inverse problem, we propose to solve it by the maximum a posteriori criterion. The distortion caused by coding is modeled as additive, spatially correlated Gaussian noise, while the original image is modeled as a high order Markov random field based on the fields of experts framework. Experimental results show that the proposed method, in most cases, achieves higher PSNR gain than other methods and the processed images possess good visual quality. In addition, we examine the noise model used and its parameter setting. The noise model assumes that the DCT coefficients and their quantization errors are independent. This assumption is no longer valid when the coefficients are truncated. We explain how this problem can be rectified using the current parameter setting.     

基于视觉感知的高效视频编码标准帧内量化矩阵优化方法

该文使用一种基于离散余弦变换(DCT)的调制传递函数(MTF)改进高效视频编码标准(HEVC)帧内量化矩阵,并在计算过程中采用一种新的空间频率计算方法。HEVC整数DCT变换是在DCT变换的基础上经过伸缩扩展以及微调后得到的,与DCT变换有所区别,因此针对这种差别进一步优化校正量化矩阵。仿真过程中设计一种基于结构相似性(SSIM)的Bjontegaard比特率差值(BDBR)方法来测试量化矩阵的编码性能,结果表明在相同的视频质量下,该文设计的量化矩阵能进一步降低码率。该方法在编码过程中只需要简单地改变量化矩阵,没有影响编码模型的算法结构,也不会增加编码的复杂度。     

Joint Scene and Signal Modeling for Wavelet-Based Video Coding with Cellular Neural Network Architecture

This paper presents a joint scene and signal modeling for the design of an adaptive quantization scheme applied to the wavelet coefficients in subband video coding applications. The joint modeling includes two integrated components: the scene modeling characterized by the neighborhood binding with Gibbs random field and the signal modeling characterized by the matching of the wavelet coefficient distribution. With this joint modeling, the quantization becomes adaptive to not only wavelet coefficient signal distribution but also the prominent image scene structures. The proposed quantization scheme based on the joint scene and signal modeling is accomplished through adaptive clustering with spatial neighborhood constraints. Such spatial constraint allows the quantization to shift its bit allocation, if necessary, to those perceptually more important coefficients so that the preservation of scene structure can be achieved. This joint modeling enables the quantization to reach beyond the limit of the traditional statistical signal modeling-based approaches which often lack scene adaptivity. Furthermore, the dynamically enforced spatial constraints of the Gibbs random field are able to overcome the shortcomings of the artificial block division which are usually the major source of distortion when the video is coded by block-based approaches at low bit rate. In addition, we introduce a cellular neural network architecture for the hardware implementation of this proposed adaptive quantization. We prove that this cellular neural network does converge to the desired steady state with the suggested update scheme. The adaptive quantization scheme based on the joint scene and signal modeling has been successfully applied to videoconferencing application and very favorable results have been obtained. We believe that this joint modeling-based video coding will have an impact on many other applications because it is able to simultaneously perform signal adaptive and scene adaptive quantization.