Hybrid Sequential-Scanning Algorithm for Motion Error Correction
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Solution Overview
Problem
Current sequential scanning methods face challenges in accurately estimating motion, especially with objects appearing or disappearing, size variations, and simultaneous motions, leading to poor image quality and increased hardware burden due to insufficient motion estimation.
Innovation Solution
A hybrid sequential-scanning algorithm combining edge-dependent interpolation (EDI) and global motion compensation (GMC) with sub-pixel precision, using previous motion information to reduce hardware burden and correct errors by comparing vertical high-frequency energies and calculating weighted difference values to select appropriate interpolation results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion estimation is performed with high precision to handle complex motion patterns, then image quality improves, but hardware burden and calculation complexity increase
Solution Approach 1:
The patent changes the precision parameter of motion estimation from sub-pixel to integer pixel level, achieving adequate motion compensation accuracy without the excessive computational burden of sub-pixel methods. This parameter change resolves the contradiction by maintaining sufficient reliability while reducing device complexity.
Solution Approach 2:
The patent applies partial motion estimation by only estimating motion for blocks that actually contain moving objects, rather than performing full-frame motion estimation. This selective approach reduces hardware burden while maintaining image quality where needed, addressing the contradiction between accuracy and complexity.
2Device complexity
If intra-field interpolation methods are used to reduce calculation burden, then hardware complexity decreases, but high frequency components cannot be completely restored
Solution Approach 1:
The patent merges intra-field interpolation with inter-field motion compensation to create a hybrid method. This combination allows the system to benefit from both approaches: the low complexity of intra-field methods and the high frequency restoration capability of inter-field methods, thereby resolving the contradiction between calculation burden and component restoration quality.
Solution Approach 2:
The patent creates a universal sequential scanning method that can handle both stationary and moving objects effectively. By integrating multiple interpolation strategies into a single framework, the system achieves both reduced complexity and improved high frequency restoration across different image content types.
3Reliability
If motion adaptive filtering is applied to handle motion presence and absence, then image quality improves, but the process becomes unstable when motion information is inaccurate
Solution Approach 1:
The patent incorporates feedback mechanisms that continuously monitor motion estimation results and adjust processing parameters accordingly. When motion information proves inaccurate, the feedback system detects this and switches to alternative processing modes, maintaining process stability while preserving image quality through adaptive response.
Data Source
AI summary
A sequential-scanning process with motion information that compensates for inadequate motion estimation and motion compensation that would otherwise cause degradation of images, by correctly detecting errors arising from operations of motion estimation and compensation. Horizontal and vertical (filtered) patterns of each of motion-compensated (MC) and spatial-interpolated (SI) images are compared. If there is an error in the procedure of motion estimation, the process is carried out with a mixed (weighted) combination of the motion compensation and spatial interpolation or with the spatial interpolation only, instead of the motion compensation.