{"title":"基于cnn的VVC内编码快速CU划分算法","authors":"Jun Xu, Guoqing Wu, Chen Zhu, Yan Huang, Li Song","doi":"10.1109/ICIP46576.2022.9897378","DOIUrl":null,"url":null,"abstract":"Over a year has passed since the finalization of Versatile Video Coding (H.266/VVC), yet it is still far from practical deployment, a major reason being the excessive complexity. The flexible and sophisticated quad-tree with nested multi-type tree partitioning structure in VVC provides considerable performance gains while bringing about an exponential increase in encoding time. To reduce the coding complexity, this paper proposes a Convolutional Neural Network (CNN) based fast Coding Unit (CU) partitioning algorithm for intra coding, which accelerates CU partition through predicting the partition modes with texture information and terminating redundant modes in advance. Corresponding classifiers are designed for different CU sizes to improve prediction accuracy. Low rate-distortion performance degradation is guaranteed by introducing performance loss due to misclassification into the loss function. Experiments show that the proposed method can save encoding time ranging from 38.39% to 62.33% with 0.92% to 2.36% bit rate increase.","PeriodicalId":387035,"journal":{"name":"2022 IEEE International Conference on Image Processing (ICIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"CNN-Based Fast CU Partitioning Algorithm for VVC Intra Coding\",\"authors\":\"Jun Xu, Guoqing Wu, Chen Zhu, Yan Huang, Li Song\",\"doi\":\"10.1109/ICIP46576.2022.9897378\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Over a year has passed since the finalization of Versatile Video Coding (H.266/VVC), yet it is still far from practical deployment, a major reason being the excessive complexity. The flexible and sophisticated quad-tree with nested multi-type tree partitioning structure in VVC provides considerable performance gains while bringing about an exponential increase in encoding time. To reduce the coding complexity, this paper proposes a Convolutional Neural Network (CNN) based fast Coding Unit (CU) partitioning algorithm for intra coding, which accelerates CU partition through predicting the partition modes with texture information and terminating redundant modes in advance. Corresponding classifiers are designed for different CU sizes to improve prediction accuracy. Low rate-distortion performance degradation is guaranteed by introducing performance loss due to misclassification into the loss function. Experiments show that the proposed method can save encoding time ranging from 38.39% to 62.33% with 0.92% to 2.36% bit rate increase.\",\"PeriodicalId\":387035,\"journal\":{\"name\":\"2022 IEEE International Conference on Image Processing (ICIP)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Image Processing (ICIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIP46576.2022.9897378\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Image Processing (ICIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP46576.2022.9897378","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
摘要
多功能视频编码(H.266/VVC)的最终确定已经过去一年多了,但它离实际部署还很远,一个主要原因是过于复杂。在VVC中,灵活而复杂的四叉树嵌套式多类型树划分结构提供了相当大的性能提升,同时带来了指数级的编码时间增加。为了降低编码复杂度,本文提出了一种基于卷积神经网络(Convolutional Neural Network, CNN)的快速编码单元(coding Unit, CU)分割算法,该算法通过纹理信息预测分割模式并提前终止冗余模式,从而加快了编码单元的分割速度。针对不同的CU大小设计相应的分类器,提高预测精度。通过在损失函数中引入误分类导致的性能损失,保证了低率失真的性能退化。实验表明,该方法可节省38.39% ~ 62.33%的编码时间,比特率提高0.92% ~ 2.36%。
CNN-Based Fast CU Partitioning Algorithm for VVC Intra Coding
Over a year has passed since the finalization of Versatile Video Coding (H.266/VVC), yet it is still far from practical deployment, a major reason being the excessive complexity. The flexible and sophisticated quad-tree with nested multi-type tree partitioning structure in VVC provides considerable performance gains while bringing about an exponential increase in encoding time. To reduce the coding complexity, this paper proposes a Convolutional Neural Network (CNN) based fast Coding Unit (CU) partitioning algorithm for intra coding, which accelerates CU partition through predicting the partition modes with texture information and terminating redundant modes in advance. Corresponding classifiers are designed for different CU sizes to improve prediction accuracy. Low rate-distortion performance degradation is guaranteed by introducing performance loss due to misclassification into the loss function. Experiments show that the proposed method can save encoding time ranging from 38.39% to 62.33% with 0.92% to 2.36% bit rate increase.