Inter Prediction Candidate Lists for Parallel Motion Derivation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
High-resolution and high-quality image data transmission and storage require significant bandwidth and storage resources, necessitating more efficient video coding techniques to reduce complexity and improve encoding/decoding efficiency.
Innovation Solution
An inter prediction method involving the generation of merging candidate lists based on block location and size, with selective generation of first and second merging candidate lists, and parallel processing units to optimize motion information derivation for video coding and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional video coding techniques are used for high-resolution and high-quality image data, then image quality is maintained, but transmission cost and storage cost increase significantly
Solution Approach 1:
The current picture is divided into multiple current prediction units (PUs), and each PU is further divided into sub-PUs. This segmentation allows independent motion estimation and compensation for different regions, enabling more efficient compression while maintaining high image quality, thus reducing transmission and storage costs.
Solution Approach 2:
Motion estimation is performed in advance by comparing the current picture with reference pictures to generate motion vectors and prediction blocks before actual encoding. This preliminary action allows the encoder to identify redundant information that can be compressed more efficiently, reducing the bitstream size while preserving image quality.
2Loss of energy
If complex inter prediction techniques are applied to improve compression efficiency, then transmission cost decreases, but encoding complexity increases
Solution Approach 1:
The picture is segmented into multiple PUs and sub-PUs, allowing motion estimation to be performed independently for each unit. This segmentation reduces the overall computational complexity by breaking down a large complex problem into smaller, more manageable sub-problems, while still achieving efficient compression.
Solution Approach 2:
Motion estimation is performed selectively based on the characteristics of each PU. For some PUs, full motion estimation is conducted, while for others, simplified estimation methods are used. This partial action approach reduces encoding complexity while maintaining sufficient compression efficiency.
3Measurement precision
If motion estimation is performed for each prediction unit independently, then prediction accuracy improves, but processing time increases
Solution Approach 1:
The picture is divided into multiple PUs and sub-PUs that can be processed independently and in parallel. This segmentation maintains prediction accuracy for each region while enabling concurrent processing, thus reducing total processing time compared to processing the entire picture as a single unit.
Solution Approach 2:
The processing approach transitions from sequential processing of a single large PU to parallel processing of multiple smaller sub-PUs. This dimensional change in processing architecture allows prediction accuracy to be maintained for each sub-unit while significantly reducing overall processing time through parallel execution.
Data Source
AI summary
An inter predication method is provided. The method includes: generating a merging candidate list of a block to be predicted and corresponding to a current prediction unit (PU); deriving motion information of the block to be predicted, on the basis of one of a plurality of merging candidates constituting the merging candidate list; and performing a prediction on the block to be predicted, on the basis of the derived motion information, to generate a prediction block corresponding to the current PU.