Directional Motion Vector Filtering for Video Compression
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Solution Overview
Problem
Digital video compression often results in inaccurate object motions due to the assignment of motion vectors, especially when objects move in different directions or speeds, leading to incorrect vector assignment and poor compression ratios.
Innovation Solution
A directional motion vector filtering system that analyzes the direction of object boundaries in digital video frames by detecting color or brightness transitions, grouping surrounding pixels, and comparing motion vectors to accurately assign filtered motion vectors to pixels, thereby reducing incorrect assignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If motion vectors are assigned based on simple correlation of picture fragments between frames, then compression ratio is improved, but motion vector accuracy deteriorates
Solution Approach 1:
The patent segments the picture fragment into multiple groups of pixels based on detected object boundary directions. Instead of treating the entire fragment uniformly, it divides pixels into different groups (e.g., first group and second group) corresponding to different object boundaries, and assigns different motion vectors to each group. This segmentation allows accurate motion representation for multiple objects while maintaining compression efficiency.
Solution Approach 2:
The patent applies local quality by detecting object boundary directions at different locations within the picture fragment and assigning motion vectors specifically tailored to each local region. The motion vector assignment is adapted to the local object boundary characteristics, ensuring that pixels near different object boundaries receive appropriate motion vectors that reflect their actual motion, rather than using a single uniform motion vector for the entire fragment.
2Device complexity
If a single motion vector is assigned to the center pixel of a fragment containing multiple objects, then device complexity is reduced, but motion representation accuracy deteriorates
Solution Approach 1:
The patent segments pixels in the picture fragment into multiple groups based on object boundary directions. Instead of using a single motion vector for the center pixel, it identifies different object boundaries (first object boundary and second object boundary) and assigns different motion vectors (first motion vector and second motion vector) to different pixel groups, ensuring accurate motion representation for multiple objects while maintaining manageable processing complexity.
Solution Approach 2:
The patent performs partial action by selectively applying motion vector filtering only to pixels near detected object boundaries, rather than processing all pixels uniformly. The system detects object boundary directions and applies directional filtering primarily at boundary regions where motion ambiguity exists, reducing overall processing complexity while improving motion assignment accuracy where it matters most.
3Measurement precision
If motion vectors are filtered by comparing surrounding pixel motion vectors, then motion vector accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary action by detecting object boundary directions and grouping pixels before performing motion vector filtering. By pre-identifying which pixels are near object boundaries and their respective boundary directions, the system prepares the data structure in advance, allowing the subsequent motion vector comparison and filtering to be performed more efficiently on targeted pixel groups rather than all pixels in the fragment.
Solution Approach 2:
The patent applies local quality by performing motion vector filtering selectively at locations where object boundaries are detected, rather than uniformly across the entire picture fragment. The system concentrates processing resources on boundary regions where motion vector accuracy is most critical, while using simpler methods for interior regions, thereby improving overall accuracy without proportionally increasing total processing time.
Data Source
AI summary
An appropriate motion vector to assign to a pixel in a digital video frame is performed by a comparison of motion vectors of particular surrounding pixels. Direction of at least one of color transition or color brightness transition in the digital video frame is detected to detect direction of object boundaries in the digital video frame. The particular surrounding pixels are selected and grouped (filtered) according to the detected object boundary direction at each pixel. A comparison of the motion vectors of the surrounding pixels then provides information on which group of pixels to assign a current pixel being processed based in part on how close the motion vectors of the surrounding groups match a group pixels to which the pixel being processed belongs.