Threshold-Based Motion Vector Predictor Reordering for Efficient Coding
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Solution Overview
Problem
The existing video coding standards like HEVC face challenges in achieving optimal coding efficiency due to the impact of the make-up and order of motion vector predictor candidates, which can increase bitrate and complexity, especially with diverse candidates in the list.
Innovation Solution
A method to improve the ordering of motion vector predictor candidates by comparing adjacent candidates' costs and applying a threshold-based criterion to reorder or remove redundant candidates, maintaining a balance between complexity and coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If motion vector predictor candidates are derived with diversity to improve coding efficiency, then coding efficiency is improved, but bitrate increases when lower candidates are selected
Solution Approach 1:
The patent applies dynamic reordering of motion vector predictor candidates based on their cost values and spatial relationship. The ordering is not fixed but adapts based on the specific candidate characteristics, allowing the system to optimize the balance between coding efficiency and bitrate by positioning candidates with better cost diversity at higher priorities.
Solution Approach 2:
The patent introduces local quality differentiation by considering the spatial relationship between adjacent candidates and their individual cost values. Each candidate is evaluated and positioned based on its local properties (cost and spatial position), creating a differentiated ordering that optimizes both coding efficiency and bitrate utilization for each specific candidate set.
2Productivity
If the list of motion vector predictor candidates is extended to include more diverse candidates, then coding efficiency improves, but device complexity increases
Solution Approach 1:
The patent performs preliminary sorting and ordering of motion vector predictor candidates based on their cost values and spatial relationships before final selection. This preliminary organization reduces the complexity of processing larger candidate lists by pre-establishing an optimized order, allowing the system to handle more diverse candidates without proportionally increasing processing complexity.
3Ease of manufacture
If candidates with similar costs are placed adjacent in the list, then the list is simple to generate, but coding efficiency is reduced due to redundancy
Solution Approach 1:
The patent extracts and separates candidates with similar cost characteristics from their original positions and repositions them based on a reordered list that prioritizes cost diversity. By extracting candidates with similar costs and redistributing them according to the optimized ordering, the system eliminates redundancy while maintaining ease of generation through a systematic reordering process.
Data Source
AI summary
Improvements to the processing of predictors is disclosed. A list of predictors is obtained, the list of predictors having at least two predictors. It is determined whether to modify the list of predictors based on a criterion using a first cost related to a first predictor in the list and a second cost related to a second predictor in the list. The list of predictors is modified based on the determination. The criterion is based on a threshold value.