Fixed Pattern Detection in Motion Images for Clear Logo Boundaries
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Solution Overview
Problem
Conventional motion estimation and motion compensation technologies fail to properly handle fixed patterns in motion images, leading to issues such as halo formation and boundary holes due to resolution limitations and misjudgment, which affect the display quality and frame rate of videos.
Innovation Solution
A method and system for fixed pattern detection in motion images using multi-layer resolution frame images, employing a fixed pattern detection unit to identify and label fixed patterns through multi-layer resolution analysis, followed by erosion and conversion processes to obtain a clear fixed pattern, which is then used to guide motion estimation and compensation for improved frame rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If motion estimation and motion compensation technology is applied to improve video smoothness, then frame rate is increased and motion images are displayed more smoothly, but fixed patterns become unstable due to misjudgment causing stickiness and boundary halos
Solution Approach 1:
The patent extracts fixed patterns from motion images through detection algorithms that identify and separate static elements (logos, watermarks, UI elements) from moving content. By isolating these fixed patterns before motion compensation processing, the system prevents them from being incorrectly processed as moving objects, thereby maintaining their stability while still enabling frame rate enhancement through MEMC for the dynamic portions of the image.
2Measurement precision
If conventional logo detection technology is used to separate fixed images, then fixed patterns can be identified, but the detected logo pattern becomes too wide exceeding the original range due to low resolution
Solution Approach 1:
The patent applies local quality analysis by examining different regions of the image with varying detection thresholds and parameters. The system adapts its detection sensitivity locally to distinguish between true fixed patterns and false detections, using contextual information from surrounding areas to refine the detected pattern boundaries and eliminate excessive width caused by low-resolution artifacts.
Solution Approach 2:
The patent introduces temporal dimension analysis by comparing fixed pattern positions across multiple frames. By tracking pattern locations over time and comparing consistency, the system can distinguish genuine fixed patterns from transient artifacts, thereby reducing false detections and refining the detected pattern area to match the original logo dimensions more accurately.
3Measurement precision
If edge-detection method is performed on logo pattern, then boundaries can be identified, but the portion inside boundary is wrongly determined as not being logo causing pattern drift during motion compensation
Solution Approach 1:
The patent performs preliminary actions by detecting and marking fixed pattern regions before motion compensation processing begins. The system pre-identifies the complete logo area including internal portions, and creates protection masks or flags for these regions. This preliminary identification ensures that during subsequent motion estimation and compensation operations, the entire marked region (including interior areas that might be incorrectly identified by edge detection alone) is protected from being treated as moving content, thereby preventing pattern drift.
Data Source
AI summary
A method and a system for fixed pattern detection in motion images are provided. In the method, continuous motion images are obtained, and frames at multi-layer resolutions can be retrieved from the motion images. Multiple fixed patterns in multiple frames at the multi-layer resolutions can be detected based on features of previous and current frames. A first fixed pattern at a higher resolution can be to a lower-resolution fixed pattern, and the lower-resolution fixed pattern is merged with a second fixed pattern at the same lower resolution so as to generate a merged fixed pattern. The merged fixed image at the lower resolution is eroded and converted to a higher-resolution image. A boundary detection process is performed on the higher-resolution image. The first fixed image at the higher resolution is eroded according to a result of boundary detection so as to obtain a clear fixed pattern.