Video Encoder Merge Candidates Using Averaged Motion Vectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video coding technologies face challenges in optimizing coding efficiency and decoding speed, particularly in managing motion vector information and correction processing for prediction images, which affects the overall performance of video encoding and decoding processes.
Innovation Solution
The encoder and decoder derive an average value of motion vector values of two prediction candidates and register this as new motion vector information, along with associated correction processing information, to enhance the prediction candidate list, allowing for more efficient selection of appropriate motion vectors and correction processing for each block.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional motion vector selection methods are used in H.265/HEVC, then the encoding process is simpler, but coding efficiency deteriorates due to inability to select optimal motion vectors from expanded candidate lists
Solution Approach 1:
The encoder performs preliminary generation of expanded prediction candidate lists including new candidates (first and second new prediction candidates) before the actual encoding decision. This preliminary action prepares multiple motion vector options in advance, allowing the decoder to select the optimal one without increasing decoding complexity, thus improving coding efficiency while maintaining manageable encoding/decoding processes
Solution Approach 2:
The invention changes the parameter of prediction candidate list composition by introducing new prediction candidates with different motion vector derivation methods (e.g., using motion vectors from different reference pictures or different spatial/temporal positions). This parameter change expands the search space for optimal motion vectors, improving coding efficiency without requiring fundamentally new encoding/decoding architectures
2Reliability
If more prediction candidates are added to the candidate list, then the likelihood of selecting optimal motion vectors increases, but the processing complexity increases
Solution Approach 1:
The invention applies different derivation methods to different local candidates in the prediction candidate list. Each prediction candidate (first new prediction candidate, second new prediction candidate) is derived using locally optimized methods appropriate to its specific role, rather than applying a single complex method to all candidates. This local optimization improves selection accuracy while keeping individual candidate processing relatively simple
3Manufacturing precision
If conventional correction processing is applied, then the processing speed is faster, but coding efficiency deteriorates due to inability to apply appropriate correction processing for each block
Solution Approach 1:
The invention dynamically selects and applies different correction processing methods (e.g., gradient compensation, illumination compensation) based on the characteristics of each prediction block and its corresponding motion vector. This dynamic adaptation allows the system to apply appropriate correction processing for each block, improving coding efficiency while avoiding unnecessary processing for blocks that don't require it, thus maintaining processing speed
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.