论文标题
学习的框架预测可以与视频编码的块 - 动作补偿竞争吗？
Can Learned Frame-Prediction Compete with Block-Motion Compensation for Video Coding?
论文作者
论文摘要
鉴于学习视频预测的最新进展，我们研究了基于先前编码/解码的框架使用预训练的深层模型的简单视频编解码器，而无需发送任何动作侧面信息就可以基于Block-Motion补偿来与标准视频编解码器竞争。给出的框架预测给出的框架差异是由标准静止图像（Intra）编解码器编码的。实验结果表明，具有对称复杂性的简单编解码器的利率分数表现平均比10 MPEG测试视频上的X264编解码器要好得多，但尚未达到X265编解码器的水平。该结果证明了学习框架预测（LFP）的力量，因为与运动补偿不同，LFP不使用当前图片中的信息。分析了使用L1，L2或联合L2和对抗性损失对预测性能和压缩效率的培训的含义。
Given recent advances in learned video prediction, we investigate whether a simple video codec using a pre-trained deep model for next frame prediction based on previously encoded/decoded frames without sending any motion side information can compete with standard video codecs based on block-motion compensation. Frame differences given learned frame predictions are encoded by a standard still-image (intra) codec. Experimental results show that the rate-distortion performance of the simple codec with symmetric complexity is on average better than that of x264 codec on 10 MPEG test videos, but does not yet reach the level of x265 codec. This result demonstrates the power of learned frame prediction (LFP), since unlike motion compensation, LFP does not use information from the current picture. The implications of training with L1, L2, or combined L2 and adversarial loss on prediction performance and compression efficiency are analyzed.
