HMVP Candidate List Construction for Video Decoding
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Solution Overview
Problem
High-resolution, high-quality image/video compression techniques are needed to efficiently compress and transmit or store images/videos, especially for applications like virtual reality, artificial reality, and broadcasting, as existing methods increase costs due to higher data requirements.
Innovation Solution
The method involves configuring prediction candidates using History-based Motion Vector Prediction (HMVP) for inter prediction, omitting the pruning process to reduce complexity, and updating motion information in the HMVP candidate list to improve image/video coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If HMVP candidate list is configured using traditional methods with pruning process, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts and removes the pruning process from the HMVP candidate list configuration. By taking out the complex pruning operation that evaluates and removes candidates based on multiple criteria, the system maintains only the essential candidate selection mechanism, thereby reducing computational complexity while preserving adequate prediction accuracy.
Solution Approach 2:
Instead of applying full pruning processes to all HMVP candidates, the patent applies a simplified approach where only necessary candidates are retained. This partial action approach avoids the excessive computational effort of complete pruning while still achieving sufficient prediction performance for the current block.
2Manufacturing precision
If high-resolution image/video data is transmitted using existing methods, then image quality is maintained, but transmission and storage costs increase
Solution Approach 1:
The patent employs feedback mechanisms in the motion vector prediction process by utilizing history-based motion vectors from previously encoded blocks. This feedback loop allows the system to learn from past prediction errors and improve subsequent predictions, achieving better compression efficiency for high-resolution content while maintaining image quality.
Solution Approach 2:
The system performs preliminary action by pre-configuring HMVP candidate lists using historical motion information before the actual encoding of high-resolution blocks. This advance preparation reduces the computational burden during real-time encoding and improves compression efficiency, allowing high-quality transmission with reduced data requirements.
3Measurement precision
If pruning process is applied to HMVP candidates, then candidate quality is improved, but processing time increases
Solution Approach 1:
The patent segments the candidate selection process into essential and non-essential components. By dividing the HMVP configuration into core candidate selection (retained) and pruning operations (removed), the system processes only the necessary segments, thereby reducing processing time while maintaining adequate candidate quality for effective motion compensation.
Data Source
AI summary
An image decoding method performed by a decoding apparatus according to the present disclosure comprises the steps of: constructing an AMVP candidate list comprising at least one AMVP candidate for a current block; deriving an HMVP candidate list for the current block that includes HMVP candidates for the current block; selecting at least one HMVP candidate among the HMVP candidates in the HMVP candidate list; deriving an updated AMVP candidate list by adding the at least one HMVP candidate to the AMVP candidate list; deriving motion information for the current block on the basis of the updated AMVP candidate list; deriving prediction samples for the current block on the basis of the motion information for the current block; and generating reconstruction samples for the current block on the basis of the prediction samples for the current block.