Hardware Friendly Multi-Hypothesis Cross Component Prediction
Tianqi Liu, Liang Zhao, Madhu Peringassery Krishnan, Shan Ting Liu, Jing Ye, Minhao Tang · 2025
This paper introduces a novel Multi-Hypothesis Cross Component Prediction (MHCCP) method to enhance coding efficiency in image and video compression on top of AOMedia Video Model (AVM). Inspired by prior cross-component coding techniques, this work firstly presents a new cross component prediction method and then introduces a hardware-friendly design for practical deployment. The proposed MHCCP method initially generates multiple hypothesis predictions, and a linear combination of these hypothesis predictions is used to estimate the final chroma intensity. To determine the coefficients of the linear model in MHCCP, both the encoder and decoder use the Gaussian elimination method, which involves significant computational complexity. To address hardware implementation challenges, such as the high complexity for parameter derivation and limited above line buffer at the superblock boundary, the proposed method incorporates several optimizations, including decoupled vertical-horizontal prediction modes to capture diverse texture patterns, a padding mechanism to overcome line buffer constraints, and a sub-sampling strategy to reduce computational complexity. Experimental results on Common Test Condition (CTC) v7 demonstrate consistent coding gains: -0.57% (YUV-PSNR), -0.42% (Y-PSNR), -1.92% (U-PSNR), and -2.09% (V-PSNR) under all-intra settings on anchor research-v8.0.0. Notably, classes A1 and A2 achieve significant gains of -0.88% and -0.61%, respectively, highlighting the efficacy of the proposed approach.