An LSTM based Rate and Distortion Prediction Method for Low-delay Video Coding

Feiyang Liu, G. Cao, Daiqin Yang, Yiyong Zha, Yunfei Zhang, Xin Liu
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引用次数: 1

Abstract

In this paper, an LSTM based rate-distortion (R-D) prediction method for low-delay video coding has been proposed. Unlike the traditional rate control algorithms, LSTM is introduced to learn the latent pattern of the R-D relationship in the progress of video coding. Temporal information, hierarchical coding structure information and the content of the frame which is to be encoded have been used to achieve more accurate prediction. Based on the proposed network, a new R-D model parameters prediction method is proposed and tested on test model of Versatile Video Coding (VVC). According to the experimental results, compared with the state-of-the-art method used in VVC, the proposed method can achieve better performance.
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基于LSTM的低延迟视频编码速率和失真预测方法
提出了一种基于LSTM的低延迟视频编码率失真(R-D)预测方法。与传统的速率控制算法不同,引入LSTM来学习视频编码过程中R-D关系的潜在模式。利用时序信息、分层编码结构信息和待编码帧的内容来实现更准确的预测。在此基础上,提出了一种新的R-D模型参数预测方法,并在通用视频编码(VVC)测试模型上进行了测试。实验结果表明,与VVC中使用的最先进的方法相比,该方法可以获得更好的性能。
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Session details: Vision in Multimedia Domain Specific and Idiom Adaptive Video Summarization Multi-Label Image Classification with Attention Mechanism and Graph Convolutional Networks Session details: Brave New Idea Self-balance Motion and Appearance Model for Multi-object Tracking in UAV
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