Geometric Partitioning Mode Candidate Reordering
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Solution Overview
Problem
Existing video coding technologies, such as HEVC, face challenges in efficiently selecting prediction candidates for geometric prediction mode (GPM), which affects coding efficiency and quality.
Innovation Solution
The method involves reordering partitioning candidates or motion vectors based on template matching costs for geometric prediction mode (GPM). This is achieved by computing template matching costs for each candidate prediction mode and signaling the selection of a candidate prediction mode based on these costs, thereby improving candidate selection for GPM.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If template matching costs are computed for each candidate prediction mode in GPM, then coding efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by computing template matching costs for candidate prediction modes in advance, before final selection. This allows the encoder to pre-evaluate and rank multiple GPM candidates based on their matching costs, so that when actual encoding is needed, the best candidate is already identified. This resolves the contradiction by performing the computationally intensive template matching work beforehand, improving coding efficiency while managing complexity through advance preparation.
Solution Approach 2:
The patent changes parameters by computing and comparing template matching costs as a new criterion for selecting GPM candidates. Instead of using traditional motion vector selection alone, the system introduces template matching cost computation as an additional parameter that ranks candidates. This parameter change enables more accurate candidate selection and improved coding efficiency, while the cost-computation mechanism manages the added complexity through systematic evaluation.
2Manufacturing precision
If multiple candidate prediction modes are evaluated using template matching, then video quality is improved, but encoding time increases
Solution Approach 1:
The patent applies preliminary action by pre-computing template matching costs for multiple GPM candidates during a preparation phase. This allows the encoder to evaluate and rank candidates based on their matching costs before final encoding decisions are made. By performing this evaluation work in advance, the system achieves higher video quality through better candidate selection while minimizing the time impact during actual encoding operations.
Solution Approach 2:
The patent applies partial action by evaluating only the most promising candidate prediction modes using template matching, rather than exhaustively analyzing all possible candidates. The system computes template matching costs for a limited set of GPM candidates that are most likely to provide good results, thereby achieving improved video quality without the full time cost of evaluating every possible candidate. This selective evaluation balances quality improvement with encoding time constraints.
Data Source
AI summary
A method that reorders partitioning candidates or motion vectors based on template matching costs for geometric prediction mode (GPM) is provided. A video coder receives data to be encoded or decoded as a current block of a current picture of a video. The current block is partitioned into first and second partitions by a bisecting line defined by an angle-distance pair. The video coder identifies a list of candidate prediction modes for coding the first and second partitions. The video coder computes a template matching (TM) cost for each candidate prediction mode in the list. The video coder receives or signals a selection of a candidate prediction mode based on an index that is assigned to the selected candidate prediction mode based on the computed TM costs. The video coder reconstructs the current block by using the selected candidate prediction mode to predict the first and second partitions.