Image Processing Circuit Foreground Edge Motion Vector Interpolation
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Solution Overview
Problem
Conventional motion estimation and motion compensation methods for generating interpolated frames often result in a halo effect with large width, misaligned edges, and excessive edge flicker when objects move quickly, particularly due to inaccurate motion vectors at the junctions of foreground and background.
Innovation Solution
The method involves determining edges of the foreground in both frames and using motion vectors from adjacent background regions in the original frames to modify motion vectors for the outer regions of the foreground in the interpolated frame, thereby reducing the halo effect.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional motion estimation and motion compensation is used to generate interpolated frames, then frame rate is increased, but halo effect with large width occurs at foreground-background junctions
Solution Approach 1:
The patent segments the image into foreground and background regions, and further divides the background into inner region (adjacent to foreground) and outer region. By segmenting the motion vector calculation into different regions with different strategies, the patent achieves both high frame rate through efficient processing and reduced halo effect through region-specific optimization.
Solution Approach 2:
The patent applies different motion vector generation strategies to different regions: for inner background region adjacent to foreground, it uses motion vectors from the same region in reference frames; for outer background region, it uses motion vectors from corresponding positions in the interpolated frame. This local quality approach ensures optimal processing for each region, reducing halo effect at critical foreground-background boundaries while maintaining overall efficiency.
2Speed
If conventional motion estimation is used, then processing speed is maintained, but edge alignment accuracy deteriorates in interpolated frames
Solution Approach 1:
The patent performs preliminary edge detection on foreground objects in reference frames before generating the interpolated frame. By pre-identifying foreground edges and their motion characteristics, the patent can accurately align edges in the interpolated frame while maintaining processing speed through efficient preliminary processing rather than costly post-processing adjustments.
Solution Approach 2:
The patent applies enhanced edge detection and motion vector calculation specifically at foreground-background boundaries where edge alignment is critical, while using standard processing for other regions. This localized enhancement of measurement precision at critical boundaries maintains overall processing speed while improving edge alignment accuracy where it matters most.
3Object-affected harmful factors
If strong halo reduction is applied, then halo effect is reduced, but edge flicker is introduced
Solution Approach 1:
The patent dynamically adjusts motion vector parameters based on spatial position and region type. By changing motion vector source and calculation method according to location (inner vs. outer background region, foreground vs. background), the patent achieves effective halo reduction without introducing edge flicker, as the parameter changes are smooth and context-appropriate rather than abrupt and uniform.
Solution Approach 2:
The patent uses the inner background region as an intermediary between the foreground and outer background region. Motion vectors in this intermediate zone are derived from reference frames with spatial-temporal optimization, acting as a buffer that smooths transitions and prevents the abrupt changes that cause edge flicker while still reducing halo effect.
4Device complexity
If standard motion compensation is used, then computational complexity is reduced, but motion vector accuracy deteriorates at foreground-background boundaries
Solution Approach 1:
The patent segments the background into inner and outer regions relative to foreground objects, applying different motion vector generation strategies to each segment. This segmentation enables the system to maintain low computational complexity for the majority of the image (outer region using interpolated frame vectors) while improving accuracy only where needed (inner region adjacent to foreground), thus resolving the contradiction between complexity and precision.
Solution Approach 2:
The patent enhances motion vector accuracy locally at foreground-background boundaries by using motion vectors from the inner background region derived from reference frames, while maintaining standard processing elsewhere. This localized quality enhancement improves boundary accuracy without significantly increasing overall computational complexity, as the enhanced processing is applied only to boundary regions.
Data Source
AI summary
The present invention provides an image processing method, wherein the method includes the steps of: receiving an image signal including a first frame and a second frame; determining edges of a foreground in the first frame and the second frame according to at least one motion vector of the first frame and the second frame; determining an edge of the foreground in an interpolated frame according to the edge of the foreground in the first frame and the edge of the foreground in the second frame; and directly using one or more motion vectors of a background adjacent to the foreground in the first frame or the second frame to serve as one or more motion vectors of an outer region of the edge of the foreground in the interpolated frame.