基于深层特征学习的高效率视频编码中帧内快速预测算法

高效视频编码(HEVC)标准相对于H.264/AVC标准提升了压缩效率,但由于引入的编码单元四叉树划分结构也使得编码复杂度大幅度提升。对此,该文提出一种针对HEVC帧内编码模式下编码单元(CU)划分表征矢量预测的多层特征传递卷积神经网络(MLFT-CNN),大幅度降低了视频编码复杂度。首先,提出融合CU划分结构信息的降分辨率特征提取模块;其次,改进通道注意力机制以提升特征的纹理表达性能;再次,设计特征传递机制,用高深度编码单元划分特征指导低深度编码单元的划分;最后建立分段特征表示的目标损失函数,训练端到端的CU划分表征矢量预测网络。实验结果表明,在不影响视频编码质量的前提下,该文所提算法有效地降低了HEVC的编码复杂度,与标准方法相比,编码复杂度平均下降了70.96%。     

基于深层特征学习的高效率视频编码中帧内快速预测算法

高效视频编码(HEVC)标准相对于H.264/AVC标准提升了压缩效率,但由于引入的编码单元四叉树划分结构也使得编码复杂度大幅度提升.对此,该文提出一种针对HEVC帧内编码模式下编码单元(CU)划分表征矢量预测的多层特征传递卷积神经网络(MLFT-CNN),大幅度降低了视频编码复杂度.首先,提出融合CU划分结构信息的降分辨率特征提取模块;其次,改进通道注意力机制以提升特征的纹理表达性能;再次,设计特征传递机制,用高深度编码单元划分特征指导低深度编码单元的划分;最后建立分段特征表示的目标损失函数,训练端到端的CU划分表征矢量预测网络.实验结果表明,在不影响视频编码质量的前提下,该文所提算法有效地降低了HEVC的编码复杂度,与标准方法相比,编码复杂度平均下降了70.96％.     

DVC转码技术研究

本文介绍了DVC(分布式视频编码)到传统视频转码的方案,该转码方案适用于移动终端设备之间的视频通信。着重讲述了DVC到H.264的转码,针对转码过程中复杂度高和时延长等问题,利用DVC解码端生成的运动矢量来减少H.264编码的工作量,在几乎不影响视频质量的前提下,极大地降低了转码的计算复杂度和时间,提高了转码效率。同时介绍了DVC转到其它传统视频的方法和方案,最后分析了DVC转码在当前移动视频通信市场中存在的巨大潜能,以及对转码技术的未来发展和方向进行了展望。     

一种基于时域相关性的高性能视频编码快速帧间预测单元模式判决算法

为了降低高性能视频编码(HEVC)的编码计算复杂度,根据视频时域上高度相关性的特点,该文提出一种快速高性能视频编码(HEVC)帧间预测单元(PU)模式判决算法。分析了时域上相邻帧两帧相同位置编码单元(CU)的PU模式之间的相关性;同时,针对视频中可能存在对象运动,还分析了前一帧对应位置CU的周边CU与当前帧中当前CU间PU模式的相关性。根据分析的时域相关性,跳过当前CU中冗余的PU模式,从而降低编码复杂度。实验结果表明,在编码效率和峰值信噪比(PSNR)损失很小的情况下,在目前已有的HEVC快速帧间预测算法的基础上,进一步降低了31.30%的编码时间。     

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。     

一种低复杂度的HEVC帧内快速编码算法

为了降低高效视频编码(HEVC)帧内编码复杂度,提出一种HEVC帧内快速编码算法。根据视频图像的纹理复杂性,提前跳过或者中止部分尺寸的编码单元(CU)的划分,减少CU深度遍历区间;同时,根据粗选过程后预测模式和代价值的统计特性采用阈值法或者梯度模式直方图法进一步筛选掉粗选后可能性较小的预测模式,从而减少最后进行率失真(RD)代价计算的帧内预测模式数量,进一步降低编码复杂度。实验结果表明,本文算法与HEVC原始平台相比,在全I帧编码模式下编码时间平均减少42.20%,码率(BR)上升约1.75%,峰值信噪比(PSNR)降低了0.108dB,有利于实时应用。     

基于时域相关性的快速HEVC帧间模式判决方法

高效率视频编码(HEVC)相比目前的国际视频编码标准H.264/AVC,在压缩率方面有了较大的提高,但也带来了巨大的编码计算复杂度。为了降低HEVC的编码计算复杂度,提出了一种快速HEVC帧间模式判决方法。统计并分析了相邻两帧之间对应块的编码单元(CU)和预测单元(PU)的时域相关性,在此相关性基础上,跳过了一些冗余CU分割层和PU预测模式,CU层最多只取两种PU预测模式,从而大大减少了所需要进行的率失真代价计算的数量,实验结果显示,与HM7.0相比,该方法在仅损失了0.21%-1.66%的压缩率和0.01~0.09 dB的峰值信噪比的前提下,降低了52.3%~63.5%的编码时间。     

基于多分支网络的深度图帧内编码单元快速划分算法

3维高效视频编码(3D-HEVC)标准是最新的3维(3D)视频编码标准,但由于其引入深度图编码技术导致编码复杂度大幅增加。其中,深度图帧内编码单元(CU)的四叉树划分占3D-HEVC编码复杂度的90%以上。对此,在3D-HEVC深度图帧内编码模式下,针对CU四叉树划分复杂度高的问题,该文提出一种基于深度学习的CU划分结构快速预测方案。首先,构建学习深度图CU划分结构信息的数据集;其次,搭建预测CU划分结构的多分支卷积神经网络(MB-CNN)模型,并利用构建的数据集训练MB-CNN模型;最后,将MB-CNN模型嵌入3D-HEVC的测试平台,通过直接预测深度图帧内编码模式下CU的划分结构来降低CU划分复杂度。与标准算法相比,编码复杂度平均降低了37.4%。实验结果表明,在不影响合成视点质量的前提下,该文所提算法有效地降低了3D-HEVC的编码复杂度。     

一种HEVC帧内预测编码CU结构快速选择算法

为了提升高效视频编码(HEVC) 帧内预测编码部分的编码效率,提出了一种HEVC帧内预测编码编码单元(CU)结构快速选择算 法。算法通过 对CTU(coding tree unit)四叉树结构的遍历过程进行优化,设计了两种不同的最优CU结 构快速决策算 法,分别从最大划分深度和最小划分深度开始遍历,并在每一步遍历之前,判断是否提前终 止遍历操作。 同时,在对每个CTU进行求解时,依据其纹理复杂度和当前编码状态,从两种算法中选择出 最优快速决策 算法对其进行求解。在HM 15.0的基础上实现了提出的快速选择算法 。实验结果表明,本文算法能够在 保证编码性能的同时,降低31.14%的编码时间,提高了HEVC的编码效 率。     

基于零块分析的高效视频编码通信

为了实现高清、超高清视频实时编码通信传输, 针对高效视频编码(HEVC)帧间编码计算复杂度过高的问题,根据图像的文理复杂度和 编码单元的零块统计特征,提出一种新的HEVC快速帧间模式判决算法。根据Merge模式下 整单元一分为四的4个子编码单元纹 理相似度确定是否提前终止编码单元(CU)划分,同时利用帧间2N×2N预测模式下零系数与非零系数分布的区域统计特征,选择符合零块分 布特征的最佳预测单元(PU)模式。实验结果表明,在低延迟B(LDB,low-delay B)和随机访 问(RA,random access)配置条件下,提出的算法在保持编码 性能基本不变的情况下,HEVC帧间预测编码时间分别平均减少了60.2%与59.4%。     

Machine learning based fast H.264/AVC to HEVC transcoding exploiting block partition similarity

Video transcoding is to convert one compressed video stream to another. In this paper, a fast H.264/AVC to High Efficiency Video Coding (HEVC) transcoding method based on machine learning is proposed by considering the similarity between compressed streams, especially the block partition correlations, to reduce the computational complexity. This becomes possible by constructing three-level binary classifiers to predict quad-tree Coding Unit (CU) partition in HEVC. Then, we propose a feature selection algorithm to get representative features to improve predication accuracy of the classification. In addition, we propose an adaptive probability threshold determination scheme to achieve a good trade-off between low coding complexity and high compression efficiency during the CU depth prediction in HEVC. Extensive experimental results demonstrate the proposed transcoder achieves complexity reduction of 50.2% and 49.2% on average under lowdelay P main and random access configurations while the rate-distortion degradation is negligible. The proposed scheme is proved more effective as comparing with the state-of-the-art benchmarks.     

FITD: Fast Intra Transcoding from H.264/AVC to high efficiency video coding based on DCT coefficients and prediction modes

In the literatures, the designs of H.264 to High Efficiency Video Coding (HEVC) transcoders mostly focus on inter transcoding. In this paper, a fast intra transcoding system from H.264 to HEVC based on discrete cosine transform (DCT) coefficients and intra prediction modes, called FITD, is proposed by using the intra information retrieved from an H.264 decoder for transcoding. To design effective transcoding strategies, FITD not only refers block size of intra prediction and intra prediction modes, but also effectively uses the DCT coefficients to help a transcoder to predict the complexity of the blocks. We successfully use DCT coefficients as well as intra prediction information embedded in H.264 bitstreams to predict the coding depth map for depth limitation and early termination to simplify HEVC re-encoding process. After a HEVC encoder gets the prediction of a certain CU size from depth map, if it reaches the predicted depth, the HEVC encoder will stop the next CU branch. As a result, the numbers of CU branches and predictions in HEVC re-encoder will be substantially reduced to achieve fast and precise intra transcoding. The experimental results show that the FITD is 1.7–2.5 times faster than the original HEVC in encoding intra frames, while the bitrate is only increased to 3% or less and the PSNR degradation is also controlled within 0.1 dB. Compared to the previous H.264 to HEVC transcoding approaches, FITD clearly maintains the better trade-off between re-encoding speed and video quality.     

一种基于组合神经网络的HEVC帧间预测方法

高效视频编码(HEVC)作为最新视频编码标准,有着非常高的压缩效率,但是由于各种新技术的提出,其编码复杂度也大大提高。复杂度对视频编码有着重要意义,低复杂度编码的研究非常必要。利用神经网络进行HEVC的分区预测为低复杂度编码提供了有效的解决方案。文中提出了一种基于卷积神经网络(CNN)和长短期记忆网络(LSTM)的组合网络架构来对帧间分区进行预测的方法,利用自建数据库对网络进行训练;文中设计了一种预搜索模块来建立训练数据库,仿真结果表明,神经网络的精度可达87%,利用该网络架构进行帧间预测可以实现52%~71%的复杂度节省。     

Cost-efficient HEVC-based quadtree splitting (HEQUS) for VVC Video Transcoding

The release of the latest video coding standard, known as Versatile Video Coding (VVC), has created the need to convert current High Efficiency Video Coding (HEVC) content to this new standard. However, the traditional cascade transcoding pipeline is not effective due to the exorbitant computational complexity of VVC. With this in mind, this paper proposes a fast HEVC-VVC transcoder that implements a probabilistic classifier based on Naïve-Bayes at the first partitioning level (128 × 128 pixels). This model uses features extracted from the 128 × 128 size blocks of the residual and reconstructed frames in the HEVC bitstream, and their correlation with the block partitioning structure. For the subsequent VVC coding depth levels, partitioning decisions are derived from the HEVC structure. The results achieve a 57.08% transcoding time reduction with a BD-rate penalty of 2.40%, compared with a traditional transcoding approach for the random access encoding configuration.     

SVM based approach for complexity control of HEVC intra coding

The High Efficiency Video Coding (HEVC) is adopted by various video applications in recent years. Because of its high computational demand, controlling the complexity of HEVC is of paramount importance to appeal to the varying requirements in many applications, including power-constrained video coding, video streaming, and cloud gaming. Most of the existing complexity control methods are only capable of considering a subset of the decision space, which leads to low coding efficiency. While the efficiency of machine learning methods such as Support Vector Machines (SVM) can be employed for higher precision decision making, the current SVM-based techniques for HEVC provide a fixed decision boundary which results in different coding complexities for different video content. Although this might be suitable for complexity reduction, it is not acceptable for complexity control. This paper proposes an adjustable classification approach for Coding Unit (CU) partitioning, which addresses the mentioned problems of complexity control. Firstly, a novel set of features for fast CU partitioning is designed using image processing techniques. Then, a flexible classification method based on SVM is proposed to model the CU partitioning problem. This approach allows adjusting the performance-complexity trade-off, even after the training phase. Using this model, and a novel adaptive thresholding technique, an algorithm is presented to deliver video encoding within the target coding complexity, while maximizing the coding efficiency. Experimental results justify the superiority of this method over the state-of-the-art methods, with target complexities ranging from 20% to 100%.     

Self-learning residual model for fast intra CU size decision in 3D-HEVC

As an extension of the High Efficiency Video Coding (HEVC) standard, 3D-HEVC requires to encode multiple texture views and depth maps, which inherits the same quad-tree coding structure as HEVC. Due to the distinct properties of texture views and depth maps, existing fast intra prediction approaches were presented for the coding of texture views and depth maps, respectively. To further reduce the coding complexity of 3D-HEVC, a self-learning residual model-based fast coding unit (CU) size decision approach is proposed for the intra coding of both texture views and depth maps. Residual signal, which is defined as the difference between the original luminance pixel and the optimal prediction luminance pixel, is firstly extracted from each CU. Since residue signal is strongly correlated with the optimal CU partition, it is used as the feature of each CU. Then, a self-learning residual model is established by intra feature learning, which iteratively learns the features of the previously encoded coding tree unit (CTU) generated by itself. Finally, a binary classifier is developed with the self-learning residual model to early terminate CU size decision of both texture views and depth maps. Experimental results show the proposed fast intra CU size decision approach achieves 33.3% and 49.3% encoding time reduction on average for texture views and depth maps with negligible loss of overall video quality, respectively.     

An image feature-based method to efficiently determine inter-coding depth in HEVC

The quad-tree based picture partition scheme in High Efficiency Video Coding (HEVC) results in a more substantial increase in computational complexity than those incurred by its predecessor video coding standards because of the need in this scheme to determine the best coding unit (CU) partitions. In this paper, we propose a method to effectively reduce the computational complexity of inter-prediction coding in the HEVC standard. The relative displacement of the largest coding unit (LCU) at the corresponding position between adjacent frames is tested through optical flow (motion estimation). The texture intensity of the LCU at the given time is tested if the condition that determines the coding depth in advance cannot be satisfied. The depth of the coding unit (CU) can be determined in advance beyond the xCompressCU function by using our proposed method, which does not require the calculation of the rate-distortion (RD) cost for each level of depth, and thus reduces the circular traversal times of the xCompressCU function. Experimental results proved that our proposed method is effective, as it reduced the computational complexity of an encoder by 53.2% on average, and had a slight influence on coding performance.     

基于灰度差值的HEVC快速帧间编码研究

针对新一代视频压缩编码标准HEVC计算复杂度较高的特点,利用视频序列间时域上的相关性,提出了一种基于灰度差值的编码单元快速划分策略.该策略根据当前编码块与参考块之间的灰度差值进行运动条件判决,在进行编码之前提前确定当前编码单元的编码深度信息,减少帧间预测编码的次数,从而有效地降低了编码端的计算复杂度.实验结果表明,该算法在编码效率和峰值信噪比(PSNR)损失都很小的情况下,和HM标准中的帧间预测算法相比,平均降低了50.18％的编码时间.