Adaptive Motion Estimation for Regular Patterned Images
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Solution Overview
Problem
Existing motion estimation methods fail to accurately estimate the movement of objects with regular designs, leading to errors and degradation in image quality due to the uniform cost function calculation, which incorrectly identifies the region of object movement.
Innovation Solution
A method that extracts patterns from frames, classifies them into basic patterns, calculates the correlation between macroblocks, and adjusts the weights of the SAD and SC terms in the cost function to accurately estimate motion, preventing errors caused by regular designs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a uniform cost function with fixed SAD and SC term weights is used for motion estimation, then the calculation is simple and fast, but motion estimation accuracy degrades for objects with regular designs
Solution Approach 1:
The patent applies dynamics by making the cost function weights adaptive rather than fixed. The SAD term coefficient and SC term coefficient are dynamically adjusted based on the detected pattern type in each macroblock. This allows the system to automatically adapt the cost function behavior to different content characteristics, resolving the contradiction between simple calculation and accurate motion estimation for regular designs.
Solution Approach 2:
The patent changes the parameters of the cost function based on pattern detection results. By detecting whether a macroblock contains regular patterns and accordingly modifying the SAD and SC term coefficients, the system transforms a static cost function into a content-aware one, improving motion estimation accuracy without significantly increasing computational complexity.
2Measurement precision
If the SAD term weight is increased to detect motion in regular design regions, then motion detection sensitivity improves, but false motion detection increases in stationary regions with regular patterns
Solution Approach 1:
The patent applies local quality by applying different cost function weights to different spatial regions based on their pattern characteristics. Instead of using a uniform weight across the entire image, the system detects patterns locally in each macroblock and applies appropriate SAD and SC term coefficients only to those regions, thereby improving motion detection sensitivity where needed while maintaining reliability in other regions.
Solution Approach 2:
The patent changes the cost function parameters conditionally based on local pattern detection. When regular patterns are detected in a macroblock, the system adjusts the SAD and SC term coefficients specifically for that region, allowing high sensitivity for motion detection in patterned areas while avoiding false detections in stationary areas through appropriate coefficient selection.
Data Source
AI summary
Provided are a method for motion estimation and an apparatus for processing an image. The method includes extracting a pattern from any one frame, classifying the extracted pattern as any one of predetermined basic patterns, counting basic patterns of regions included in macroblocks according to types of the basic patterns to set representative basic patterns of the macroblocks, calculating at least one of a sum of absolute difference (SAD) term coefficient and a smoothness constraint (SC) term coefficient from a correlation between representative basic patterns of macroblocks included in a macroblock group, and calculating a cost function result using at least one of the calculated SAD term coefficient and SC term coefficient.