Motion Vector Estimation Using Resolution Scaling and Sliding Window
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Solution Overview
Problem
The computationally intensive task of motion estimation in video compression is complex due to the challenge of searching for motion vectors in high-dimensional data, which consumes significant power and processing resources, especially when dealing with television quality video data.
Innovation Solution
The method involves reducing the resolution of reference and target frames, using a sliding window to compare blocks, and determining motion vectors based on numerical metrics, which are then adjusted back to original resolution for encoding, thereby reducing computational complexity and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion estimation is performed by searching for motion vectors in the entire macroblock starting at (0,0) location, then motion vector accuracy is improved, but power consumption and central processing resources increase significantly
Solution Approach 1:
The patent divides the macroblock into multiple regions or zones, and performs motion estimation selectively in different regions rather than uniformly across the entire macroblock. This segmentation allows the system to focus computational resources on areas where motion vectors are most needed while reducing processing in areas with less motion activity, thereby maintaining accuracy where required while reducing overall power consumption.
Solution Approach 2:
The patent applies different motion estimation strategies to different parts of the macroblock based on local characteristics. Regions with high motion activity receive more intensive processing to ensure accurate motion vectors, while regions with low motion activity use simplified or skipped processing. This local quality approach ensures motion vector accuracy is maintained where necessary while reducing power consumption in areas where full processing is not needed.
2Measurement precision
If motion estimation searches the entire macroblock for motion vectors, then motion estimation accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary actions by first identifying regions of interest or high-motion areas within the macroblock before conducting full motion estimation. This preliminary step allows the system to pre-select areas that require detailed motion analysis, avoiding unnecessary computations in static or low-motion regions. As a result, motion estimation accuracy is maintained in critical areas while overall processing time is reduced.
Solution Approach 2:
The patent applies partial action by performing complete motion estimation only in necessary regions rather than across the entire macroblock. By applying motion estimation selectively to specific areas where it provides the most value, the system achieves sufficient accuracy for the application while significantly reducing computational complexity and processing time compared to exhaustive search methods.
3Manufacturing precision
If high resolution video data is processed in real-time, then video quality is maintained, but network stress and processing burden increase
Solution Approach 1:
The patent segments the video processing task into multiple stages and regions, applying different processing intensities to different parts of the video data. By dividing the macroblock into regions and applying selective motion estimation, the system maintains high video quality in areas requiring detailed analysis while reducing processing burden in other areas, enabling real-time processing of high-resolution video without overwhelming network and computational resources.
Solution Approach 2:
The patent dynamically adjusts processing parameters such as motion estimation search range, block size, and processing intensity based on local video content characteristics. In regions with complex motion or high importance, parameters are set to maintain high quality, while in simpler regions, parameters are reduced to lower processing burden. This adaptive parameter adjustment enables real-time processing of high-resolution video while maintaining acceptable quality across the entire frame.
Data Source
AI summary
Methods and related systems for encoding video streams are described. Reference and target frames of a video stream are scaled to reduce their resolution. A sliding window is used to compare the reduced resolution reference and target frames and identify blocks, outlined by a sliding window, that include similar content. A motion vector indicating the motion of the identified similar content is determined. Once the motion vector is determined, the reference and target frames are restored to their native resolution and a translated location for the motion vector is identified in the restored frames. The translated location of the motion vector may be used in encoding the frames of the video stream.