LTR Frame Block Updates for Lower-Cost Video Encoding
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Solution Overview
Problem
Existing video encoding technologies face challenges in efficiently updating long-term reference frames (LTR frames) due to high processing demands and inefficient updating methods, leading to reduced coding efficiency and increased memory usage.
Innovation Solution
The method involves logically dividing LTR frames into regions and updating only those regions that require updating based on cost evaluation of corresponding blocks in frames, using a cost criterion to determine which areas need updating, thereby producing a more efficient and cost-effective LTR frame update process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LTR frames are updated using conventional methods, then reference quality is maintained, but processing complexity and memory usage increase
Solution Approach 1:
The LTR frame is divided into multiple blocks, and each block is independently evaluated for update necessity based on motion detection. This segmentation allows the system to process only relevant portions of the frame, reducing overall processing complexity while maintaining reference quality in updated regions.
Solution Approach 2:
Different regions of the LTR frame are treated differently based on their update requirements. Blocks with significant changes are updated with high quality, while blocks without changes retain their existing data. This local quality approach ensures reference quality is maintained where needed without unnecessary processing elsewhere.
2Reliability
If LTR frames are updated frequently, then reference quality is improved, but processing time and computational cost increase
Solution Approach 1:
Motion detection and update necessity evaluation are performed preliminarily before actual frame updates. By identifying which blocks require updates in advance, the system avoids unnecessary processing of static regions, significantly reducing processing time while ensuring reference quality is improved only where changes occur.
Solution Approach 2:
Instead of updating the entire LTR frame, the system performs partial updates only on blocks that contain motion or changes. This partial action approach reduces computational cost and processing time while maintaining reference quality in the critical updated regions.
3Measurement precision
If complete LTR frame updates are performed, then encoding accuracy is improved, but memory usage and processing overhead increase
Solution Approach 1:
The LTR frame is segmented into multiple blocks that are independently managed in memory. Only blocks requiring updates are loaded and processed, reducing memory usage compared to loading and processing the entire frame. This segmentation enables encoding accuracy to be maintained in updated blocks without the memory overhead of complete frame updates.
Solution Approach 2:
The system discards blocks that do not require updates and retains only the blocks that need updating for processing. After updates are applied, the modified blocks are recovered and integrated into the LTR frame. This approach reduces memory usage by eliminating unnecessary data while maintaining encoding accuracy for the updated portions.
Data Source
AI summary
Techniques for Long Term Reference (LTR) frame updating in a video encoding process are performed by an image processing device as part of the video encoding process. The method comprises encoding a first LTR frame. The method comprises encoding a plurality of frames referencing directly or indirectly to the first LTR frame. The method comprises sequentially updating the first LTR frame by evaluating a cost for encoding a block of image data in one of the plurality of frames and by updating an image area in the first LTR frame when the cost fulfils a cost criterion. The image area is updated based on the block of image data in at least one of the plurality of frames. The method comprises encoding the sequentially updated first LTR frame as a second LTR frame.