Label-Based Video Frame Encoding for Motion Vector Accuracy
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Solution Overview
Problem
Current video encoding techniques consume large resources and are inaccurate due to brute force comparison of current frames with previous frames to generate motion vectors, which limits parallelization and leads to high power consumption and false matches.
Innovation Solution
A label-based approach is introduced where points/regions of current video frames are labeled using graphics information, allowing for efficient matching with corresponding points/regions of previous frames, reducing the search space and improving accuracy by utilizing graphics information such as draw call labels, primitive labels, model coordinates, and texture coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If brute force comparison is used to match points/regions of current frame with previous frame, then matching accuracy may be maintained, but resource consumption and power consumption increase significantly
Solution Approach 1:
The patent segments the previous frame into multiple blocks and creates labels for each block based on their spatial and temporal characteristics. This segmentation allows the current frame to be matched against specific blocks rather than performing brute force comparison with the entire previous frame, significantly reducing computational complexity and power consumption while maintaining matching accuracy through targeted comparison.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the previous frame to generate motion labels and block classifications before the actual motion estimation process. These preliminary labels organize the search space and enable faster matching in the current frame, reducing the overall computational burden and energy consumption of the video encoding process.
2Reliability
If brute force comparison is used to generate motion vectors, then comprehensive frame matching is achieved, but parallelization is limited and processing time increases
Solution Approach 1:
By dividing the previous frame into multiple blocks and assigning labels to each block, the patent enables parallel processing of multiple regions simultaneously. The current frame can be matched against labeled blocks in parallel, significantly improving processing speed while maintaining comprehensive frame matching through the organized block structure.
Solution Approach 2:
The preliminary generation of motion labels and block classifications organizes the search space before motion estimation, enabling more efficient and parallelizable processing in subsequent steps. This preliminary organization allows multiple regions to be processed simultaneously, increasing productivity without sacrificing matching completeness.
3Productivity
If labels are derived from graphics information, then matching efficiency and accuracy improve, but device complexity increases
Solution Approach 1:
The patent derives labels from graphics information through preliminary processing steps that organize spatial and temporal data before matching. This preliminary organization improves matching efficiency by providing structured information about block characteristics, while the label derivation process itself becomes a reusable component that reduces overall system complexity through standardization.
Data Source
AI summary
Disclosed is an approach for encoding a current video frame that includes labeling points/regions for the current video frame using graphics information for the current video frame, matching the points/regions of the current video frame with points/regions of a previous video frame using the labels for the points/regions of the current video frame and deriving motion vectors for the points/regions of the current video frame.