MVP Candidate Set Derivation for HEVC Video Coding
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Solution Overview
Problem
In High Efficiency Video Coding (HEVC), the existing motion vector predictor (MVP) candidate set derivation methods can lead to parsing errors and coding inefficiencies due to incomplete candidate sets, causing mismatches between encoder and decoder MVP lists, which propagate decoding errors across multiple frames.
Innovation Solution
A method to generate a complete full MVP candidate set by determining spatial neighboring blocks, removing redundant candidates, and adding pre-defined replacement candidates to ensure the set reaches a target size, with specific values assigned for AMVP and Merge modes to prevent empty spaces and redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the existing MVP candidate set derivation methods are used, then the coding process is simpler, but parsing errors and coding inefficiencies occur due to incomplete candidate sets
Solution Approach 1:
The patent applies preliminary action by defining a complete full MVP candidate set derivation method that proactively ensures all necessary candidates are available before encoding/decoding. The encoder and decoder both derive the complete candidate set using the same rules (spatial neighbors A0, A1, B0, B1, temporal neighbor T0), preventing mismatches and parsing errors before they occur.
Solution Approach 2:
The patent changes the parameter of candidate set completeness by introducing a fixed target size (5 candidates) and systematically filling all positions with valid predictors. This ensures the candidate set is always complete and consistent between encoder and decoder, eliminating parsing errors while maintaining manageable complexity.
2Productivity
If redundant MVP candidates are not removed, then the candidate set is more complete, but coding efficiency decreases due to redundancy
Solution Approach 1:
The patent applies the taking out principle by systematically removing redundant candidates from the MVP candidate set. After deriving all spatial and temporal neighbors, the method identifies and removes duplicates, keeping only unique valid predictors. This extraction of redundancy improves coding efficiency while the systematic derivation process ensures consistency between encoder and decoder.
3Reliability
If empty spaces are left in the MVP candidate set, then the derivation process is simpler, but decoding errors propagate across multiple frames
Solution Approach 1:
The patent applies preliminary action by pre-defining all candidate positions (A0, A1, B0, B1, T0) and systematically filling each position with valid predictors before encoding. This ensures no empty spaces remain in the candidate set, preventing decoding errors and propagation across frames, while maintaining organized and manageable complexity.
Data Source
AI summary
A method and apparatus derive a motion vector predictor (MVP) candidate set for a block. Embodiments according to the present invention determine a plurality of spatial neighboring blocks of the block, obtain one or more spatial MVP candidates from motion vectors associated with the spatial neighboring blocks, determine whether one or more redundant MVP candidates exist in the spatial MVP candidates, generate a first MVP candidate set, wherein said generating the first MVP candidate set comprises not including the determined one or more redundant MVP candidates into the first MVP candidate set, and generate a final MVP candidate set according to the first MVP candidate set.