计算机科学 ›› 2022, Vol. 49 ›› Issue (7): 127-131.doi: 10.11896/jsjkx.211100179
刘月红1, 牛少华2, 神显豪1
LIU Yue-hong1, NIU Shao-hua2, SHEN Xian-hao1
摘要： 为了提高虚拟现实视频帧内预测编码的性能,采用卷积神经网络算法进行视频帧编码单元(Coding Unit,CU)选择,从而降低视频图像编码复杂度。首先设置量化参数,获取虚拟现实视频帧样本,接着构建图像编码树,然后建立卷积神经网络(Convolutional Neural Network,CNN)帧编码单元优化模型,将帧样本的图像亮度作为CNN的输入,结合图像率失真成本阈值,通过训练获得帧编码单元的优化结果。采用CNN训练优化,能够根据图像不同纹理度模块的帧内编码需求,获得不同深度的编码树(Coding Tree Unit,CTU)结构及合适数量的CU模块。实验结果表明,通过合理设置卷积核尺寸和量化参数,相比常用视频帧内预测编码算法,CNN算法能够获得更优的图像质量,在Balboa序列中的BD码率和编码时间分别为56 483.76 kbps和3 209.24 s。
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