Transform kernel selection for blended intra prediction
Muho Cheon, Hongkwon Pai, Byeungwoo Jeon · 2025
In this paper, we investigate an explicit transform selection method that merges transform sets of the two intra prediction modes used for predictor blending, aimed at the new blending intra prediction (BIP) tools (i.e., DIMD, TIMD, OBIC) for the enhanced compression beyond VVC capability. The existing multiple transform selection (MTS) mechanism in ECM is designed based on the characteristics of regular intra prediction modes, which differ from those of BIP modes. Therefore, applying the existing selection method of multiple transform set to BIP modes is ineffective. The proposed method merges multiple transform sets corresponding to the two intra prediction modes used for predictor generation and makes it possible for a decoder to decide which transform kernel pair to use without explicit signaling. It enables the use of more diverse transform kernel pairs. Experimental results show performance improvements of -0.01%, 0.00%, and 0.01% for the Y, Cb, and Cr components, respectively, compared to the ECM 13.0 test model under the All Intra (AI) configuration. These results emphasize clear need for an improved MTS scheme tailored to BIP tools as well.