Description: 文章比较了H.264和AVS两个标准在运动补偿中运动矢量预测算法的差别, 提出了一种实现H.264 中
主档次(main pmille)下的第4级别(1evel 4)和AVS中的基准档次面向高清应用时运动矢量预测复用的硬件结构。
提出了一种新颖的缓存管理更新机制.极限情况下用于运动矢量的片上缓存大小减少了75% 。用FPGA验证结果
表明资源占用情况是单独实现AVS的2.3倍,是单独同时实现两个标准的70%。能实现对1080i 30Hz高清图像实
时解码。-Article compares the H. 264 and AVS standards in the two motion compensation algorithm in the motion vector prediction difference, a realization of H. 264 in the main level (main pmille) under section 4 level (1evel 4) and AVS in the benchmark grade of application-oriented high-definition motion vector prediction reuse the hardware structure. A novel cache management update mechanism. Limit used for motion vectors on-chip cache size reduction of 75. The results show that the use of FPGA resources to verify the occupancy of a separate AVS realize 2.3 times, both individually and at the same time realize the standard 70. Can realize high-definition images to 1080i 30Hz real-time decoding. Platform: |
Size: 275456 |
Author:宋恋 |
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Description: 在VC++6.0下运行的MPEG-4编码,包括入口函数,视频对象平面编码函数,运动信息编码,运动估计和预测补偿函数,运动向量和预测误差计算的函数,纹理编码,运动向量图象编码函数,帧内纹理编码函数,形状纹理编码函数,DC/AC系数预测函数,供大家参考-In VC++6.0 run under the MPEG-4 encoding, such as import function, video object plane coding function, sports information coding, motion estimation and prediction of the compensation function, motion vector and prediction error calculation function, texture coding, motion vector images encoding function, intra-coding function of texture, shape, texture coding function, DC/AC coefficient prediction function for your reference Platform: |
Size: 149504 |
Author:simon |
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Description: 本文提出一种基于H.264低比特率视频流的半脆弱盲水印方法,通过在H.264特有的的帧内预
测和运动向量预测中分别嵌入鲁棒水印和脆弱水印,达到版权保护和内容完整性认证的双重目的.通过在嵌入强
度和预测模式选择的拉格朗日乘子中引入H.264量化因子,增强了水印对重量化编码的抵抗力,取得更好的率
失真平衡,减小水印嵌入对视频流比特率的影响.该算法可以实现水印的快速嵌入提取,满足视频实时处理的要
求,实验结果证明了该算法的有效性.-A novel semi—fragile blind watermark algorithm based on H.264 low bit—rate video stream was pro—
posed in this paper,copyright protection and content authentication Can be achieved jointly by embedding the robust waterraark
an d fragile watermark spectively during the process of interior prediction an d motion vector prediction which are
characteristic for H.264 standard.Through introducing the quan tization factor of H.264 in the embedding and prediction
mod e choice,the proposed scheme Can strengthen the watermarking resistance to video recoding and strike a good rate—
distortion balan ce for the waterm ark of lower bit—rate video st/ ea/n.which relieve bit—rates lossness from the waterm arking
embedding.Experimental results also demonstrate that this scheme iS very computational efi cient during watermark—
ing embedding an d extraction. Platform: |
Size: 384000 |
Author:阿国 |
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Description: This project seeks to implement H.264 (the standard x264 is based on) to run on CUDA. Specifically we attempt to write the motion estimation module in CUDA.
Motion estimation is currently the most computationally intensive portion of the H.264 encoding process. Previous attempts to parallelize this algorithm in CUDA showed sizeable speedups but sacrificed quality by ignoring the motion vector prediction (MVp). We demonstrate the viability of a hierarchical (pyramid) motion estimation algorithm in CUDA. This solution addresses the MVp problem while still taking advantage of the CUDA framework.-This project seeks to implement H.264 (the standard x264 is based on) to run on CUDA. Specifically we attempt to write the motion estimation module in CUDA.
Motion estimation is currently the most computationally intensive portion of the H.264 encoding process. Previous attempts to parallelize this algorithm in CUDA showed sizeable speedups but sacrificed quality by ignoring the motion vector prediction (MVp). We demonstrate the viability of a hierarchical (pyramid) motion estimation algorithm in CUDA. This solution addresses the MVp problem while still taking advantage of the CUDA framework. Platform: |
Size: 467968 |
Author:softweb |
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Description: 回归型支持向量机的简化算法,可以用于船舶运动预报-Simplification algorithm based on support vector machine regression can be used for ship motion prediction Platform: |
Size: 663552 |
Author:hukaiye |
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