Video Encoder Merge Prediction With Averaged Motion Vectors
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Solution Overview
Problem
Existing video coding technologies face challenges in optimizing encoding and decoding processes to handle increasing digital video data efficiently, particularly in selecting appropriate filters, block sizes, motion vectors, and reference pictures.
Innovation Solution
An encoder and decoder system that derives an average value of motion vector values and registers it as new motion vector information, along with correction processing information, in the prediction candidate list to enhance coding and decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional video coding standards (H.264/AVC, H.265/HEVC) are used to process digital video data, then video compression is achieved, but coding efficiency and processing speed cannot be sufficiently improved to handle increasing video data amounts
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing motion vector candidates and correction processing information in prediction candidate lists before actual encoding/decoding operations. This allows the system to quickly retrieve and select from pre-prepared options rather than computing everything in real-time, thereby improving coding efficiency without proportionally increasing processing complexity.
Solution Approach 2:
The patent introduces dynamic adaptation by allowing the encoder to selectively generate and update prediction candidate lists based on specific conditions (e.g., when certain prediction modes are used). The system dynamically adjusts the number and type of prediction candidates (including bi-directional prediction candidates with averaged motion vectors) based on the encoding context, optimizing performance for different video content scenarios.
2Manufacturing precision
If more prediction candidates and correction processing information are added to improve coding efficiency, then video quality and compression performance improve, but the complexity of encoding and decoding processes increases
Solution Approach 1:
The patent segments the prediction candidate list into different types of candidates (e.g., spatial prediction candidates, temporal prediction candidates, bi-directional prediction candidates) with specific roles. Each segment serves a particular prediction purpose, allowing the decoder to efficiently select appropriate candidates based on the prediction mode without being overwhelmed by a monolithic list of all possible candidates.
Solution Approach 2:
The patent changes parameters by introducing new motion vector calculation methods (averaging motion vectors from multiple reference pictures) and new prediction candidate types (bi-directional prediction candidates). These parameter changes enhance prediction accuracy and coding precision while maintaining manageable complexity through structured organization and selective application.
3Productivity
If traditional motion vector selection methods are used in merge mode, then encoding simplicity is maintained, but coding efficiency and decoding speed are insufficient for handling increasing video data
Solution Approach 1:
The patent performs preliminary actions by pre-generating multiple motion vector candidates and correction processing information during the encoding phase and storing them in prediction candidate lists. This allows the decoding phase to simply retrieve and select from pre-computed options, significantly improving decoding speed while the encoder handles the additional complexity of candidate generation.
4Manufacturing precision
If conventional prediction methods without averaged motion vectors are used, then encoding complexity is reduced, but coding efficiency and video quality are insufficient
Solution Approach 1:
The patent introduces dynamic motion vector processing by calculating averaged motion vectors from multiple reference pictures and prediction candidates based on specific encoding conditions. This dynamic approach allows the system to adaptively improve prediction accuracy when beneficial while maintaining simpler processing when conditions don't warrant the additional complexity.
Data Source
AI summary
An encoder includes circuitry and memory connected to the circuitry. In operation, the circuitry: derives an average value of motion vector values of two prediction candidates in a prediction candidate list for a merge mode, and registers the average value derived as new motion vector information of a new prediction candidate into the prediction candidate list; and derives new correction processing information regarding correction processing of a prediction image, and registers the new correction processing information derived into the prediction candidate list in association with the new motion vector information.