Merge Candidate Block Derivation via Motion Estimation Region
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
High-resolution and high-quality video transmission and storage become costly due to increased video data, and existing video compression techniques do not efficiently handle merge candidate block processing.
Innovation Solution
A method and apparatus for deriving a merge candidate block by decoding motion estimation region (MER) information, determining if a prediction object block and spatial merge candidate block are in the same MER, and adaptively replacing or determining spatial merge candidate blocks based on MER and prediction object block sizes, allowing parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional sequential processing is used for deriving merge candidate blocks, then processing accuracy is maintained, but computational complexity and processing time increase
Solution Approach 1:
The picture is divided into multiple motion estimation regions (MERs) that can be processed independently and in parallel. Each MER contains prediction units that can be handled separately, enabling parallel processing architecture while maintaining processing accuracy through region-based decomposition
Solution Approach 2:
Motion estimation region information is decoded and prepared in advance before merge candidate block derivation. The MER configuration is predetermined and stored, allowing subsequent parallel processing to proceed without sequential dependencies, thus reducing computational complexity and processing time
2Manufacturing precision
If video resolution and quality are increased to HD and UHD levels, then video quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The video picture is segmented into multiple motion estimation regions that can be processed independently. This segmentation enables more efficient compression by allowing different processing strategies for different regions, reducing the overall data volume required for high-resolution video while maintaining quality
Solution Approach 2:
The patent changes the processing parameters by introducing motion estimation region divisions and using parallel processing approaches. This allows for more efficient compression ratios in high-definition video, reducing transmission and storage costs while preserving video quality through optimized prediction and coding parameters
3Ease of operation
If spatial merge candidate blocks from the same MER are used, then processing simplicity is maintained, but parallel processing capability is reduced
Solution Approach 1:
By dividing the picture into multiple motion estimation regions, the patent enables parallel processing across different MERs while maintaining simple processing within each region. The segmentation creates natural boundaries that allow independent processing of candidate blocks from different regions, achieving both simplicity and parallelism
Solution Approach 2:
The motion estimation region acts as an intermediary structure that organizes prediction units into separate processing groups. This intermediary layer enables parallel processing by preventing dependencies between different MERs, while maintaining processing simplicity within each region through standardized candidate block selection
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present invention relates to a method for inducing a merge candidate block and a device using same. An image decoding method involves decoding motion estimation region (MER) related information; determining whether or not a predicted target block and a spatial merge candidate block are included in the same MER; and determining the spatial merge candidate block to be an unavailable merge candidate block when the predicted target block and the spatial merge candidate block are included in the same MER. Accordingly, by parallely performing the method for inducing a merge candidate, parallel processing is enabled and the computation amount and implementation complexity are reduced.