Walsh-Hadamard transform （WriT） can solve linear error equations on Field F2, and the method can be used to recover the parameters of convolutional code. However, solving the equations with many unknowns needs enorm...Walsh-Hadamard transform （WriT） can solve linear error equations on Field F2, and the method can be used to recover the parameters of convolutional code. However, solving the equations with many unknowns needs enormous computer memory which limits the application of WriT. In order to solve this problem, a method based on segmented WriT is proposed in this paper. The coefficient vector of high dimension is reshaped and two vectors of lower dimension are obtained. Then the WriT is operated and the requirement for computer memory is much reduced. The code rate and the constraint length of convolutional code are detected from the Walsh spectrum. And the check vector is recovered from the peak position. The validity of the method is verified by the simulation result, and the performance is proved to be optimal.展开更多
基金supported by the National Natural Science Foundation of China(1127105011371183+2 种基金61403036)the Science and Technology Development Foundation of CAEP(2013A04030202013B0403068)
基金supported by the National Natural Science Foundation of China(61072120)
文摘Walsh-Hadamard transform （WriT） can solve linear error equations on Field F2, and the method can be used to recover the parameters of convolutional code. However, solving the equations with many unknowns needs enormous computer memory which limits the application of WriT. In order to solve this problem, a method based on segmented WriT is proposed in this paper. The coefficient vector of high dimension is reshaped and two vectors of lower dimension are obtained. Then the WriT is operated and the requirement for computer memory is much reduced. The code rate and the constraint length of convolutional code are detected from the Walsh spectrum. And the check vector is recovered from the peak position. The validity of the method is verified by the simulation result, and the performance is proved to be optimal.
文摘现有的基于卷积神经网络(convolutional neural network,CNN)的环路滤波器倾向于将多个网络应用于不同的量化参数(quantization parameter,QP),消耗训练模型中的大量资源,并增加内存负担。针对这一问题,提出一种基于CNN的QP自适应环路滤波器。首先,设计一个轻量级分类网络,按照滤波难易程度将编码树单元(coding tree unit,CTU)划分为难、中、易3类;然后,构建3个融合了特征信息增强融合模块的基于CNN的滤波网络,以满足不同QP下的3类CTU滤波需求。将所提出的环路滤波器集成到多功能视频编码(versatile video coding,VVC)标准H.266/VVC的测试软件VTM 6.0中,替换原有的去块效应滤波器(deblocking filter,DBF)、样本自适应偏移(sample adaptive offset,SAO)滤波器和自适应环路滤波器。实验结果表明,该方法平均降低了3.14%的比特率差值(Bjøntegaard delta bit rate,BD-BR),与其他基于CNN的环路滤波器相比,显著提高了压缩效率,并减少了压缩伪影。
