Fusion-Based Intra Transform Selection Using HoG and VIPM
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video coding systems face challenges in efficiently compressing digital video signals due to inefficiencies in predicting video blocks and selecting appropriate transform sets, leading to suboptimal compression and decoding performance.
Innovation Solution
The implementation of cross-component residual prediction using a fusion of prediction blocks, histogram of gradients (HoG), and virtual intra prediction mode (VIPM) to determine a transform set for video encoding and decoding, allowing for the selection of optimal transform sets based on prediction modes and fusion weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fusion of multiple prediction blocks is used to predict a video block, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent determines HoG and VIPM from prediction blocks before the fusion operation. By performing these calculations in advance on individual prediction blocks rather than on the fused result, the computational complexity is reduced while still achieving accurate transform set selection for the final fused prediction
Solution Approach 2:
The patent processes each prediction block separately to determine its HoG and VIPM, then fuses the prediction blocks. This segmentation approach allows the system to handle complex multi-block predictions by breaking them into manageable individual block analyses, reducing overall computational burden
2Productivity
If multiple transform sets are selected based on VIPM and HoG, then compression performance is improved, but processing time increases
Solution Approach 1:
The patent determines HoG and VIPM from prediction blocks before the fusion operation, enabling early selection of appropriate transform sets. This preliminary determination reduces processing time by avoiding repeated HoG calculations on fused blocks while still achieving optimal compression through multiple transform set selection
Solution Approach 2:
The patent uses HoG parameters to determine VIPM, which then guides transform set selection. By changing the approach from direct transform selection to VIPM-based selection, the system achieves better compression performance while managing processing time through parameter-driven decision making
3Measurement precision
If HoG is determined from the first prediction block before fusion, then transform set selection accuracy is improved, but computational overhead increases
Solution Approach 1:
The patent determines HoG from the first prediction block before the fusion operation. This preliminary HoG determination provides sufficient information for accurate transform set selection without requiring HoG calculations on all fused prediction blocks, thereby reducing computational overhead while maintaining selection accuracy
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
Systems, devices, and methods are described herein for multiple transform set selection for fusion based intra modes. An example device may determine that a fusion of a plurality of prediction blocks is to be used to predict a video block. The device may determine a prediction block of the plurality of prediction blocks using a prediction mode. The device may determine a histogram of gradients (HoG) based on the prediction block, for example, before the fusion of the plurality of prediction blocks. The device may determine a virtual intra prediction mode (VIPM) based on the HoG. The device may determine a transform set based on the VIPM. The device may decode the video block using one of a plurality of transform sets, wherein the plurality of transform sets comprises the determined transform set.