Abstract
Recently, deep image compression has shown a big progress in terms of coding efficiency and image quality improvement. However, relatively less attention has been put on video compression using deep learning networks. In the paper, we first propose a deep learning based bi-predictive coding network, called BP-DVC Net, for video compression. Learned from the lesson of the conventional video coding, a B-frame coding structure is incorporated in our BP-DVC Net. While the bi-predictive coding in the conventional video codecs requires to transmit to decoder sides the motion vectors for block motion and the residues from prediction, our BP-DVC Net incorporates optical flow estimation networks in both encoder and decoder sides so as not to transmit the motion information to the decoder sides for coding efficiency improvement. Also, a bi-prediction network in the BP-DVC Net is proposed and used to precisely predict the current frame and to yield the resulting residues as small as possible. Furthermore, our BP-DVC Net allows for the compressive feature maps to be entropy-coded using the temporal context among the feature maps of adjacent frames. The BP-DVC Net has an end-to-end video compression architecture with newly designed flow and prediction losses. Experimental results show that the compression performance of our proposed method is comparable to those of H.264, HEVC in terms of PSNR and MS-SSIM.
Abstract (translated)
近年来,深图像压缩在编码效率和图像质量改善方面取得了很大的进展。然而,使用深度学习网络对视频压缩的关注相对较少。本文首先提出了一种基于深度学习的视频压缩双预测编码网络BP-DVC网络。借鉴传统视频编码的经验,在BP-DVC网络中引入了B帧编码结构。传统视频编解码器中的双预测编码要求将块运动的运动矢量和预测的余数传给译码器端,而我们的BP-DVC网络在译码器端和译码器端都采用了光流估计网络,不需要将运动信息传给译码器端进行编码。效率改进。同时,在BP-DVC网络中提出了一种双预测网络,用于精确预测当前帧,并尽可能小地产生剩余量。此外,我们的BP-DVC网络允许使用相邻帧的特征图之间的时间上下文对压缩特征图进行熵编码。BP-DVC网络具有端到端视频压缩架构,具有新设计的流量和预测损失。实验结果表明,该方法在PSNR和MS-SSIM方面的压缩性能与H.264、HEVC的压缩性能相当。
URL
https://arxiv.org/abs/1904.02909