结合空时域下采样与重建的H.264/AVC压缩性能优化

为提高H.264/AVC标准在带宽资源严重受限时的压缩效率,采用空时域相结合的编码思路,提出了一种基于运动检测的自适应抽帧方法,并结合空域下采样与重建研究了一种改进的H.264/AVC压缩性能优化框架。在编码端,原视频先空域下采样以减少空间分辨率,然后根据视频运动特征,采用不同抽帧模式自适应地降低帧率,再经H.264/AVC编码,有效降低了编码码率。在解码端,解码视频则采用与抽帧模式相对应的运动估计与补偿插帧方法重建出抽取帧,再利用超分辨率重建技术将视频恢复到原空间分辨率。实验结果表明,所提方法在低码率段的视频压缩性能优于H.264/AVC标准编解码及相关文献方法。     

结合卷积神经网络的HEVC帧内编码压缩改进算法

近年来,卷积网络深度学习已在图像处理、目标检测等领域取得巨大成功。受其启发,将卷积神经网络(CNN)应用于传统视频压缩标准已成为一个新的研究热点。本文提出一种集成卷积神经网络的高效视频编码(HEVC)压缩改进算法,将下采样过程、HEVC的编解码过程、上采样及质量增强过程集成为一体。为高效提取视频帧的结构特征,在所提压缩算法中集成了两个卷积神经网络。提出了一种下采CNN(DwSCNN)代替双三次下采,在有效降低分辨率的同时保留细节信息,得到更为紧凑的低分辨率视频序列,将此低分辨率视频序列通过HEVC帧内编码进行进一步的数据量压缩,通过提出一个质量增强CNN(PPCNN)来改善解码后恢复到原始分辨率的降质视频序列。实验结果显示,本文压缩改进算法在低码率段与标准HEVC相比,能达到更好的质量重建,并且在接近一致的PSNR值时,能节省39.46%的时间和11.04%的比特率,本文算法的视频压缩性能优于HEVC标准算法和相关文献方法。     

基于对象的水下视频低码率编码算法

由于水下声波信道带宽窄，难以采用高效视频编码(High Efficiency Video Coding, HEVC)实现水下视频低码率传输。提出了一种基于对象的水下视频低码率编码算法。首先对水下对象视频进行时空域下采样以降低其数据量，再采用低延时模式编码少量视频帧作为参考帧。然后，提取水下对象视频非参考帧的特征点，并对特征点和对象掩膜进行编码。在解码端用特征点和掩膜进行对象的粗糙重建，获得对象的初步轮廓和颜色信息。最后，根据粗糙重建对象和参考帧对象的映射关系，采用基于在线学习的方法实现对象的精细重建。实验结果表明，与HEVC相比，所提算法的BDBR-SSIM (Bjontegarrd Delta Bit Rate and Structural Similarity)降低了14.88%。     

基于H.264和双树小波的多描述视频编码

本文针对互联网和无线信道等不可靠网络的视频传输问题,提出一种基于H.264和双树小波变换的多描述视频编码解决方案.采用分层的多描述视频编码框架,实现H.264和双树小波编码的有机结合.基本层用H.264编码器对视频信号进行低码率编码后,复制到各个描述;增强层用三维双树小波变换对原视频和基本层重建视频的差值进行编码,将产生的四棵三维小波树经噪声整形后两两组合,编码送到不同描述.在解码端,若能够接收到两个描述,则通过中心解码器实现高质量的视频重建;若丢失一个描述,则通过边解码器解码仍可保证一定质量的视频重建.实验结果表明在相同码率下,本算法的视频中心解码和边解码质量优于现有的多描述视频编码算法.     

HEVC并行解码器设计

新一代视频编码标准HEVC(High Efficiency Video Coding)主要面向高清及超高清视频编码,压缩效率相比之前的编码标准H.264有较大提高.但压缩效率的提高必然会带来计算的复杂化,为提高HEVC的解码效率,降低时延,提出了一种并行解码器架构.该并行解码器的设计是基于HEVC中熵片(Entropy slice)和波前并行处理(Wavefront Parallel Processing, WPP)技术的引入以及滤波器(Deblocking Filter)无相关性的特点.实验结果表明,该并行解码器能够充分利用硬件资源,提高解码效率.     

浅谈下一代编码压缩技术——HEVC

下一代的高效视频编码技术(HEVC)已经悄然而生了,它将带给编码压缩行业一个惊人的成果:比现在的MPEG-4,H.264.AVC编码方式还要节省50%的有效码流。这是技术发展的必然趋势。HEVC将对更高标准的视频传输带来可能性,比如将同样带宽内的标清视频数量提升数倍,将高清视频压缩到标清的码率,将4K视频直接通过一个节目通道传输,等等。这都将给广播电视行业带来又一次革命性的提升.     

浅析超高清数字电视视频压缩编码技术

近期超高清数字电视成为各国研究的热点,由于超高清电视具有超高分辨率的特点,造成了其信号数据量庞大,原视频编码标准H.264/AVC已经无法满足高压缩比的要求,必须采用新的视频压缩编码方案。新一代视频压缩标准HEVC正是面向高清和超高清视频图像而被提出,它可以保证在与H.264相同视频质量的前提下,使得视频流的码率减少50%。重点介绍了超高清电视的视频压缩编码技术HEVC,包括HEVC的四叉树块分割结构、帧内预测编码技术和滤波技术。     

参数重用的HEVC多描述视频编码

为了提高视频编码的容错性能,保证视频经不可靠信道传输后的重建质量。本文提出了一种面向高效视频编码标准（High Efficiency Video Coding,HEVC）的基于参数重用的多描述视频编码方法。原始视频进行空间梅花下采样,生成四个行列交错的子序列,其中两个子序列采用标准编码器进行编码,并在编码过程中提取视频中每个编码单元（Coding Unit,CU）的深度信息、预测单元（Predicting Unit,PU）的分割方式以及帧内预测模式。而其余两个子序列利用已编码的视频序列信息,进行简化的编码过程。选取一个经标准编码的子序列,与一个简化编码的子序列,结合生成描述1,其余子序列生成描述2,不同描述分信道传输。多描述的编码结构可以保证即使只接收到单一描述也能保证视频的重建质量,参数重用的方法利用子序列间的相关性,减少了冗余信息,降低了编码开销。实验结果表明,参数重用的HEVC多描述视频编码针对高清视频编码效果明显,边缘解码质量PSNR值仅略低于中心解码0.7 dB,有效地提高了高清视频编码的容错性能。进行简化编码子序列的平均编码时间节省了91.7%,实现了高编码效率、低复杂度的HEVC容错编码。      

一种HEVC快速编码单元划分方法

新一代视频编码标准HEVC(High Efficiency Video Coding)巨大的编码优势将成为数字视频产业的首选编解码标准.压缩率的提高导致了计算复杂度的大幅度增加,因此本文针对HEVC视频编码标准的特性,基于四叉树的编码结构,通过将编码单元的划分与SSIM(Structural Similarity index,结构相似度)相结合,优化了帧内编码效率.     

基于HEVC标准视频框架下帧内预测技术的研究

新一代视频编码标准HEVC采纳当前视频主流压缩技术研究的新成果,使其编码性能基本达到H.264/AVC标准的2倍,然而随着这种压缩性能提高的同时也加剧了编码计算的复杂度。该文针对此难题,对HEVC在块划分层面和预测模式筛选上的优化进行了研究,以期提高编码速度。     

基于层修复技术的DVC鲁棒传输框架

针对分布式视频编码(DVC)系统鲁棒传输问题,设计了一种基于层修复的DVC系统传输框架。该传输框架首先对关键帧(K帧)同时采用高效视频编码(HEVC)帧内编码和Wyner-Ziv编码,并将校验信息(Wyner-Ziv码流)作为修复层码流存入缓存中。若当前关键帧有丢失,则向编码端请求该帧对应的层修复码流,在解码端对错误块进行修复,获得关键帧解码质量的提升。同时,研究了层修复码率估计算法,利用已成功解码的位平面辅助完成算法重建。实验结果表明,该传输框架利用关键帧的层修复码流对关键帧失真部分进行了修复,提高了关键帧质量,改善了边信息质量,实现了DVC的鲁棒传输。     

基于模式对应与机器学习的HEVC降分辨率转码算法

HEVC是ITU-T VCEG 继H.264之后所制定的新的视频编码标准,它提高了视频的编码效率,在相同视频质量的前提下,压缩比与H.264相比提高了一倍。另外,随着4G网络的兴起和智能手机的普及,移动终端成为人们观看网上视频的一大主流平台。但是,网络中存储的视频分辨率普遍要大于移动终端屏幕分辨率,为解决这个问题,本文开展了针对HEVC的降分辨率转码研究工作,利用高分辨率视频的编码信息,通过模式对应来简化低分辨率视频的编码模式的计算过程,并采用机器学习的方法来确定降分辨率时的组块阈值,以提高模式对应的准确性。实验结果表明,提出的算法与Trivial transcoder相比,在保持PSNR和比特率几乎不变的同时,编码时间平均节省了60%左右。     

Foveation scalable video coding with automatic fixation selection

Image and video coding is an optimization problem. A successful image and video coding algorithm delivers a good tradeoff between visual quality and other coding performance measures, such as compression, complexity, scalability, robustness, and security. In this paper, we follow two recent trends in image and video coding research. One is to incorporate human visual system (HVS) models to improve the current state-of-the-art of image and video coding algorithms by better exploiting the properties of the intended receiver. The other is to design rate scalable image and video codecs, which allow the extraction of coded visual information at continuously varying bit rates from a single compressed bitstream. Specifically, we propose a foveation scalable video coding (FSVC) algorithm which supplies good quality-compression performance as well as effective rate scalability. The key idea is to organize the encoded bitstream to provide the best decoded video at an arbitrary bit rate in terms of foveated visual quality measurement. A foveation-based HVS model plays an important role in the algorithm. The algorithm is adaptable to different applications, such as knowledge-based video coding and video communications over time-varying, multiuser and interactive networks.     

Efficient reconstruction scheme for transform domain Wyner-Ziv video coding

To minimize the errors of the reconstructed values and improve the quality of decoded image,an efficient reconstruction scheme for transform domain Wyner-Ziv (WZ) video coding is proposed.The reconstruction scheme exploits temporal correlation of the coefficient bands,the WZ decoded bits stream and the side information efficiently.When side information is outside the decoded quantization bin,the reconstructed value is derived using expectation of the WZ decoded bit stream and the side information.When side information is within the decoded quantization bin,the reconstructed value is derived using the biased predictor.Simulation results show that the proposed reconstruction scheme gains up to 1.32 dB compared with the commonly used boundary reconstruction scheme at the same bit rates and similar computation cost.     

Cross residual transform for lossless intra-coding for HEVC

A new lossless intra-coding method based on a cross residual transform is applied to the next generation video coding standard HEVC (High Efficiency Video Coding). HEVC includes a multi-directional spatial prediction method to reduce spatial redundancy by using neighboring pixels as a prediction for the pixels in a block of data to be encoded. In the new lossless intra-coding method, the spatial prediction is performed as pixelwise DPCM but is implemented as block-based manner by using cross residual transform on the HEVC standard. The experimental results show that the new lossless intra-coding method reduces the bit rate of approximately 8.43% in comparison with the lossless-intra coding method in the HEVC standard and the proposed method results in slightly better compression ratio than the JPEG200 lossless coding.     

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.     

Piecewise DC prediction in HEVC

In this paper, we propose an improved DC prediction method for high-efficiency video coding (HEVC) intra coding. The technique involves the application of pixel-wise predictors rather than block-based predictor in the DC mode. In HEVC, the pixels of neighboring reconstructed blocks are used to support multiple directional spatial prediction modes and reduce spatial redundancy. For lossless coding, pixel-by-pixel differential pulse code modulation (DPCM) is applied to directional predictions. Consequently, residuals are reduced and coding efficiency is improved. Since DC prediction still employs block-based coding, pixel-wise DC prediction is needed for better coding efficiency. When compared to HEVC lossless intra coding, the proposed algorithm reduces the bit rate by 5.95%. Furthermore, with pixel-by-pixel DPCM, the average bit rate reduction is 10.64% when compared to HEVC lossless intra coding.     

An SAD‐Based Selective Bi‐prediction Method for Fast Motion Estimation in High Efficiency Video Coding

As the next‐generation video coding standard, High Efficiency Video Coding (HEVC) has adopted advanced coding tools despite the increase in computational complexity. In this paper, we propose a selective bi‐prediction method to reduce the encoding complexity of HEVC. The proposed method evaluates the statistical property of the sum of absolute differences in the motion estimation process and determines whether bi‐prediction is performed. A performance comparison of the complexity reduction is provided to show the effectiveness of the proposed method compared to the HEVC test model version 4.0. On average, 50% of the bi‐prediction time can be reduced by the proposed method, while maintaining a negligible bit increment and a minimal loss of image quality.