• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Fast adaptive block based motion estimation for video compression.
【24h】

Fast adaptive block based motion estimation for video compression.

机译：用于视频压缩的基于快速自适应块的运动估计。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this dissertation, a new block-based motion estimation (ME) method is proposed which uses the Kalman filtering (KF) with adaptive block partitioning (ABP) to improve the motion estimates resulting from the conventional block-matching algorithms (BMAs). In our work, a first-order autoregressive (AR) model is applied to the motion vectors (MVs) obtained by BMAs. A new approach is developed for adaptively adjusting the state parameters of the Kalman filter and the motion correlations between neighboring blocks are referred to predict motion information. According to the statistics of the frame MVs, 16x16 macro-blocks (MBs) are split into 8x8 blocks or 4x4 sub-blocks adaptively for fine grain operation of the Kalman filtering. To further improve the performance of MV prediction, we adopt a zigzag scanning of blocks or sub-blocks and the state parameters of the Kalman filter are updated successively during each iteration in accordance with the outcome of the zigzag based block or sub-block scanning. The experimental results indicate that the proposed method can effectively improve the ME performance in terms of the peak-signal-to-noise-ratio (PSNR) of the motion compensated images with smoother motion vector fields as compared to the existing approaches in the literature. The scheme described herein is also tested on high resolution video samples yielding least motion artifacts in the reconstructed image frames. Such robust performance makes it an ideal temporal redundancy extraction engine for a wide variety of video transmissions and new digital TV applications.;From micro to nano-scale medium development point of view, however, the block-based KF motion prediction is not the most cost effective and fastest approach for mobile video communications and computing devices including the third (G3) and fourth (G4) generation technology standards. To this end, as a second part of the research, we focused on developing a fast binary partition tree based variable size video coding system. New adaptive algorithms proposed herein are applied to a video encoder with binary partition trees. First, to reduce the computation for block-matching, an adaptive search area method is described which adjusts the searching region according to the size of each block. Second, an early termination method is introduced which terminates the binary partitioning process adaptively according to the statistics of the peak-signal-to-noise-ratio values during each step of block splitting. Third, we put forward a new model for fast rate-distortion (R-D) estimation to decrease the computation of matching pursuit (MP) coding for residual images. Simulation results show that the proposed techniques provide significant gain in computation speed with little or no sacrifice of R-D performance, when compared with non-adaptive binary partitioning scheme.
机译：本文提出了一种新的基于块的运动估计（ME）方法，该方法将卡尔曼滤波（KF）与自适应块划分（ABP）结合使用，以改进传统块匹配算法（BMA）产生的运动估计。在我们的工作中，将一阶自回归（AR）模型应用于BMA获得的运动矢量（MV）。开发了用于自适应地调整卡尔曼滤波器的状态参数的新方法，并且将相邻块之间的运动相关性称为预测运动信息。根据帧MV的统计，自适应地将16x16宏块（MB）分成8x8块或4x4子块，以进行卡尔曼滤波的精细操作。为了进一步提高MV预测的性能，我们采用对块或子块进行之字形扫描，并根据基于之字形块或子块扫描的结果在每次迭代过程中连续更新Kalman滤波器的状态参数。实验结果表明，与文献中的现有方法相比，该方法可以在运动矢量图像场更平滑的情况下，有效地改善运动补偿图像的峰值信噪比（PSNR）。还在高分辨率视频样本上测试了本文所述的方案，该高分辨率视频样本在重建的图像帧中产生最少的运动伪像。如此强大的性能使其成为用于各种视频传输和新型数字电视应用的理想时间冗余提取引擎。;从微米到纳米级介质开发的角度来看，基于块的KF运动预测并不是最有效的方法适用于移动视频通信和计算设备的经济高效且最快的方法，包括第三代（G3）和第四代（G4）技术标准。为此，作为研究的第二部分，我们专注于开发基于快速二进制分区树的可变大小视频编码系统。本文提出的新的自适应算法被应用于具有二进制分区树的视频编码器。首先，为了减少用于块匹配的计算，描述了一种自适应搜索区域方法，该方法根据每个块的大小来调整搜索区域。其次，引入了一种提前终止方法，该方法根据在块分割的每个步骤中峰值信号与噪声比的统计值来自适应地终止二进制分割过程。第三，提出了一种新的快速率失真估计模型，以减少残差图像的匹配追踪编码的计算。仿真结果表明，与非自适应二进制分区方案相比，所提出的技术可显着提高计算速度，而几乎不牺牲R-D性能。

著录项

  • 作者

    Luo, Yi.;

  • 作者单位

    Ohio University.;

  • 授予单位 Ohio University.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 200 p.
  • 总页数 200
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号