论文标题
用于多功能视频编码的基于深度学习的内部模式推导
Deep Learning-Based Intra Mode Derivation for Versatile Video Coding
论文作者
论文摘要
在内部编码中，进行速率失真优化（RDO）以从预定义的候选列表中获得最佳的内部模式。除残留信号外，还需要对最佳内部模式进行编码和发送到解码器侧的编码，并消耗大量的编码位。为了进一步提高多功能视频编码（VVC）中内部编码的性能，本文提出了一种智能的内部模式推导方法，称为基于深度学习的内部模式推导（DLIMD）。在具体而言，内部模式派生的过程是作为多类分类任务提出的，该任务旨在跳过用于降低编码位的内部模式信号传导的模块。开发了DLIMD的体系结构是为了适应不同的量化参数设置和可变编码块，包括非方面的编码块，这些块由一个单个训练有素的模型处理。与现有的基于深度学习的分类问题不同，手工制作的功能除了从功能学习网络中学习的功能外，还可以进入内部模式推导网络。为了与传统方法竞争，在视频编解码器中使用了另一个二进制标志，以指示与RDO的所选方案。广泛的实验结果表明，所提出的方法可以实现VVC测试模型平均值的平均Y，U，U和V组件的2.28％，1.74％和2.18％的比特率降低，这表现优于最先进的作品。
In intra coding, Rate Distortion Optimization (RDO) is performed to achieve the optimal intra mode from a pre-defined candidate list. The optimal intra mode is also required to be encoded and transmitted to the decoder side besides the residual signal, where lots of coding bits are consumed. To further improve the performance of intra coding in Versatile Video Coding (VVC), an intelligent intra mode derivation method is proposed in this paper, termed as Deep Learning based Intra Mode Derivation (DLIMD). In specific, the process of intra mode derivation is formulated as a multi-class classification task, which aims to skip the module of intra mode signaling for coding bits reduction. The architecture of DLIMD is developed to adapt to different quantization parameter settings and variable coding blocks including non-square ones, which are handled by one single trained model. Different from the existing deep learning based classification problems, the hand-crafted features are also fed into the intra mode derivation network besides the learned features from feature learning network. To compete with traditional method, one additional binary flag is utilized in the video codec to indicate the selected scheme with RDO. Extensive experimental results reveal that the proposed method can achieve 2.28%, 1.74%, and 2.18% bit rate reduction on average for Y, U, and V components on the platform of VVC test model, which outperforms the state-of-the-art works.
