Adaptive Motion Detection for Image Quality and Speed
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Solution Overview
Problem
Existing image processing methods face challenges in accurately determining the motion condition of images, leading to incorrect selection of luminance/chrominance separation methods, which results in image distortion and quality issues due to noise interference.
Innovation Solution
A method for detecting image motion by comparing pixel value variations and averages between target and reference frames, using a pixel-based approach to determine the motion condition of individual pixels, thereby selecting the appropriate Y/C separation method to maintain image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If 2D Y/C separation is used for motion images, then processing speed is improved, but image quality deteriorates due to boundary blurring and cross-color phenomena
Solution Approach 1:
The patent applies dynamics by making the Y/C separation method adaptive rather than static. The system dynamically switches between 2D and 3D separation methods based on real-time motion detection results. When motion is detected in a block, 2D separation is used for speed; when still, 3D separation is used for quality. This dynamic adaptation resolves the contradiction between processing speed and image quality.
Solution Approach 2:
The patent changes the parameter of separation method selection based on motion detection. By calculating motion magnitude from pixel differences between frames and comparing it to a threshold, the system adjusts which separation algorithm to apply. This parameter-based switching allows optimal performance for both motion and still image conditions.
2Manufacturing precision
If 3D Y/C separation is used for still images, then image quality is improved, but processing complexity increases
Solution Approach 1:
The patent segments the image processing into blocks and performs motion detection at the block level rather than for the entire image. By dividing the image into manageable segments and applying 3D separation only to still blocks while using 2D separation for motion blocks, the system reduces overall processing complexity while maintaining quality where needed.
Solution Approach 2:
The patent applies local quality by using different separation methods for different regions of the image based on their motion characteristics. Still regions receive the computationally intensive 3D separation for high quality, while motion regions use the faster 2D separation. This localized approach optimizes the balance between quality and complexity.
3Manufacturing precision
If motion detection is performed to select Y/C separation method, then image quality is improved, but measurement precision deteriorates due to noise interference
Solution Approach 1:
The patent performs motion detection at multiple levels: first at the block level to determine overall motion status, and then potentially at the pixel level for refined separation. By using a two-stage approach with block-level detection as the primary filter, the system achieves sufficient motion detection accuracy without being overly sensitive to noise that would plague purely pixel-level detection.
Solution Approach 2:
The patent introduces motion magnitude calculation as an intermediary step between raw pixel comparison and final separation method selection. By computing the magnitude of motion and comparing it against a threshold, the system creates a buffer that filters out noise-induced false detections while preserving genuine motion detection, thereby improving measurement precision.
Data Source
AI summary
The invention discloses motion detecting method and apparatus thereof. The method and apparatus are utilized to detect a motion condition of a target pixel. The motion detecting method comprises the following steps: receiving a composite signal including a target frame and a reference frame; selecting a first reference pixel from the target frame and selecting a second reference pixel and a third reference pixel from the reference frame; generating a first signal according to the pixel values of the target pixel and the first reference pixel; calculating a first difference between the target pixel and the first reference pixel and a second difference between the second reference pixel and the third reference pixel, and generating a second signal according to the first difference and the second difference; selecting a plurality of first pixels from the target frame and a plurality of second pixels from the reference flame, according to the target pixel, wherein the second pixels correspond to the first pixels; generating a third signal according to the averaged value of the first pixels and the averaged value of the second pixels; and determining a motion condition of the target pixel according to the first signal, the second signal, and the third signal.