Fusion-Based Versatile Video Coding Intra Prediction Algorithm with Template Matching and Linear Prediction

Dan Luo, Shuhua Xiong*, Chao Ren, Raymond Sheriff, Xiaohai He

*Corresponding author for this work

Research output: Contribution to journalArticle (journal)peer-review

1 Citation (Scopus)
41 Downloads (Pure)


The new generation video coding standard Versatile Video Coding (VVC) has adopted many novel technologies to improve compression performance, and consequently, remarkable results have been achieved. In practical applications, less data, in terms of bitrate, would reduce the burden of the sensors and improve their performance. Hence, to further enhance the intra compression
performance of VVC, we propose a fusion-based intra prediction algorithm in this paper. Specifically, to better predict areas with similar texture information, we propose a fusion-based adaptive template matching method, which directly takes the error between reference and objective templates into account. Furthermore, to better utilize the correlation between reference pixels and the pixels to be predicted, we propose a fusion-based linear prediction method, which can compensate for the deficiency of single linear prediction. We implemented our algorithm on top of the VVC Test Model (VTM) 9.1. When compared with the VVC, our proposed fusion-based algorithm saves a bitrate of
0.89%, 0.84%, and 0.90% on average for the Y, Cb, and Cr components, respectively. In addition, when compared with some other existing works, our algorithm showed superior performance in bitrate savings.
Original languageEnglish
Article number5977
Number of pages15
Issue number16
Early online date10 Aug 2022
Publication statusPublished - 10 Aug 2022


  • Versatile Video Coding
  • Intra Prediction
  • Template Matching
  • Linear Prediction


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