基于菱形编码的视频信息隐藏算法

针对基于视频帧内预测模式调制的信息隐藏算法嵌入容量较小、比特率上升较明显等问题,提出一种基于菱形编码的帧内视频信息隐藏算法。该算法基于高效视频编码（HEVC）,将相邻两个4×4块预测模式组成模式对,采用改进的菱形编码算法指导模式调制和信息嵌入过程;并采取二次编码方式在保留原始平台最优编码划分下进行第二次隐秘信息嵌入编码,在保证嵌入量的同时抑制帧内失真漂移。实验结果表明：所提算法峰值信噪比（PSNR）值下降在0.03dB以内,码率增长低于0.53%,嵌入量有大幅提升,并能很好地保证视频主客观质量。     

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.     

HEVC帧内预测算法的优化

对最新视频编码标准HEVC的帧内预测算法作了分析,提出一种基于编码块的纹理特征和相邻已编码块深度的帧内预测快速算法。在全I帧模式下,该算法在HEVC校验模型(HM2.0)上的实验结果表明:在保证与原算法的性能相差不大的情况下,编码时间平均可以缩减29%。     

非正方形四叉树变换系数能量分布模型及其扫描方式改进

传统的非正方形四叉树变换(NSQT)系数的扫描方式并不能很好地适应系数的能量分布,这将降低熵编码的编码效率。为了提高编码效率,该文提出非正方形四叉树变换系数能量分布模型,并改进了其系数扫描方式。首先推导了非正方形变换块的空间频率域位置分布,基于推导的分布,从理论上提出了其能量分布模型,并用实验进行了证实,发现能量从左上角到右下角逐渐递减,而左下角的能量与右上角的能量接近,根据该能量分布模型,重建了非正方形四叉树变换中量化系数的扫描模式,以适应熵编码,而熵编码中的系数扫描方式倾向于系数上的降序。实验结果显示,相比NSQT中已有的系数扫描方式,该文方法提出的扫描方式在编码计算复杂度和客观视频质量损失可忽略的前提下,平均下降了1.32%~3.74%的比特率,对于非正方形变换块,下降了9.05%~13.19%的比特率。     

最新一代视频压缩标准VP9综述

VP9是一个由谷歌开发的开放格式、无使用授权费的视频压缩标准,并做为谷歌推行WebM工程的一部分.对VP9标准的主要技术特征进行了分析,包括帧内预测、帧间预测、变换和量化、熵编码、环路滤波等,同时与其他先进的视频压缩标准HEVC进行了比较.     

Reference picture selection with decreased temporal dependency for HEVC error resilience

The high level of compression efficiency achieved by the High-Efficiency Video Coding standard (HEVC) decreases the robustness of the encoded bitstreams. This increased susceptibility to network errors leads to end video quality degradation. Moreover, due to the high computational complexity of HEVC, high-resolution video transmission with time constraints over hostile channels such as wireless networks becomes more challenging. This paper proposes a reference picture selection-based error-resilient method to reduce the temporal error propagation due to high-trip delay and frame-copy concealment error. First, the encoder selects the reference pictures based on the error status received from the feedback channel, taking into consideration the Rate-Distortion-Optimization (RDO). Second, the temporal information mismatch prediction resulting from the error concealment is reduced by decreasing the temporal dependency between adjacent frames based on new motion-estimation tools. Results show a PSNR gain of about 6.13 dB, 5.20 dB and 4.72 dB for 1080p, 720p and 480p resolutions respectively.     

Fast algorithm based on the sole- and multi-depth texture measurements for HEVC intra coding

In High Efficiency Video Coding (HEVC), intra coding plays an important role, but also involves huge computational complexity due to a flexible coding unit (CU) structure and a large number of prediction modes. This paper presents a fast algorithm based on the sole- and multi-depth texture measurements to reduce the complexity from CU size and prediction mode decisions. For the CU size decision, evaluation results in the CU coding with one and multiple depths are utilized to classify CUs into heterogeneous, homogeneous, depth-prominent and other ones. Fast CU size decisions are made for different kinds of CUs. For the prediction mode decision, the tendencies for different CU sizes are detected based on multiple depths. The number of searching modes is decreased adaptively for the CU size with fewer tendencies. Experimental results show the proposed algorithm by off-line training reduces 53.32% computational complexity, with 1.47% bit-rate increasing.     

基于机器学习的HEVC帧内模式快速决策算法

针对高效视频编码(HEVC)计算复杂度过高的情况, 提出了一种基于机器学习的帧内快速决策算法。根据图像内容的平滑 程度将PU划分成3类,对具有一定平滑度的预测单元(PU)不需要遍历完所有的帧内预测模式 ,从而有效降低算法的计算复杂度。首 先,计算各个PU的左边参考像素方差、上边参考像素方差和总参考像素的方差,以及各个P U采用的最优的帧内预测模式, 这些方差反映了参考像素的平滑程度;然后,利用机器学习软件Weka对得到的数据进行分类 处理,得到分类决策树; 最后,根据决策树来判定各个PU需要测试的帧内模式,再对各个PU 遍历这些帧内模式,确 定最优的模式,减少不必要 预测,从而降低编码复杂度。实验结果表明,本文算法相对于标准的HEVC 15.0编码算法,在高码率的情况下,编码时间平 均节省约16.18%,BD-rate平均升高约0.25%,BD-PSNR平均降低约0.02 dB;在低码率的情况下,编码时 间平均节省约20.75%,BD-rate平均升高 约0.04%,BD-PSNR平均降低约0.00dB。     

一种基于零块分布的快速帧间模式判决算法

针对新一代视频编码标准离散视编码(HEVC)帧间预 测编码计算复杂度过高的问题,提出一种利用变换量化 后零块分布特征进行快速帧间模式筛选的低复杂度算法。首先统计SKIP和帧间 2N×2N模式下零系数与 非零系数的区域分布特征,判断其运动特征和运动估计匹配效果,用以分析全零块下SKIP 模式的分布;同 时在非全零块下结合残差块的SATD代价分类筛选PU模式分布,从而优先选择符合零块分布 特征的PU 模式,以达到快速筛选帧间模式,降低计算复杂度的效果。实验结果表明,在低延迟和随机 访问配置条件下,本文提出的算法在保证编码性能的情况下,相比原有HM 快速算法, 编码时间平均分别可减少50.3和49.3%,BDBR仅降低0.03%,有效 地降低了编码复杂度。