Video Frame Processing Component for Motion Estimation Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video processing technologies face inefficiencies in quickly and accurately comparing pixel data between frames for motion estimation, particularly in high-resolution video processing, which affects the performance of motion search algorithms and image correlation operations.
Innovation Solution
A processing component that calculates comparison metrics for video frames by blending reference frame pixels with corresponding pixels from previous frames using motion vector data, enabling efficient determination of output frame pixels through a combination of absolute difference calculations and motion compensation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional SAD comparison operation is used to compare pel data between frames, then motion estimation can be performed, but processing efficiency decreases and processing time increases for high-resolution video
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing cumulative sum data and edge detection data for blocks of pixels before actual motion estimation is needed. This preprocessing allows the motion estimation algorithm to quickly retrieve pre-computed values instead of performing lengthy calculations during real-time video processing, thereby reducing processing time while maintaining accuracy
Solution Approach 2:
The patent segments the video processing task by dividing the frame into blocks of pixels and further dividing each block into sub-blocks. This segmentation allows parallel processing of multiple blocks simultaneously and enables the use of block-based cumulative sum data structures, which significantly reduces the computational complexity of SAD calculations compared to processing individual pixels sequentially
2Speed
If block-based motion estimation is performed to reduce computational complexity, then processing speed improves, but measurement precision of motion vectors decreases
Solution Approach 1:
The patent segments each motion estimation block into multiple sub-blocks and performs motion estimation on each sub-block independently. This fine-grained segmentation allows the system to achieve both computational efficiency through block-based processing and high measurement precision through sub-block level analysis, effectively resolving the contradiction between speed and accuracy
Solution Approach 2:
The patent applies local quality by using different processing strategies for different regions within a block. Edge detection data is computed and stored for each block, allowing the system to identify and handle edge regions with higher precision while using simpler calculations for uniform regions, thereby maintaining high measurement precision across the entire frame while preserving processing speed
3Measurement precision
If high-resolution video processing is implemented to improve image quality, then measurement precision improves, but device complexity and processing requirements increase significantly
Solution Approach 1:
The patent reduces device complexity for high-resolution processing by pre-calculating cumulative sum data and edge detection data for all blocks in the frame before motion estimation begins. This preliminary action transforms the complex real-time high-resolution processing problem into a simpler retrieval and comparison operation, maintaining measurement precision while significantly reducing the computational burden on processing hardware
Solution Approach 2:
The patent manages device complexity for high-resolution video by segmenting the processing task into block-level and sub-block-level operations. By organizing data structures at the block level (with cumulative sums and edge data per block) and performing fine-grained analysis only where needed at the sub-block level, the system achieves high-resolution processing accuracy without requiring proportionally increased hardware complexity
Data Source
AI summary
Methods and apparatus for facilitating processing a reference frame to produce an output frame. Motion vector data for a block of reference frame pels estimates the displacement of the reference frame pels from corresponding pels in a prior input frame. Comparison metrics are produced for a pel of the reference frame with respect to that pel and a plurality of neighboring reference frame pels. A first comparison metric is based on a comparison with corresponding pels of a prior output frame that corresponds to the prior input frame as previously processed. A second comparison metric is based on a comparison with corresponding pels of a motion compensated prior output frame derived from applying motion vector data to the pels of the prior output frame. A pel of the output frame that corresponds to the reference frame pel is determined using the first and second comparison metrics.