Black Border Detection in Video Frames Using Dual-Stage Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques are inadequate for accurately detecting the presence and boundaries of black borders in video content, which can lead to errors in variant analysis and other applications, such as inserting secondary information.
Innovation Solution
The method involves analyzing a grayscale image and its edge image to identify candidate black borders by computing maximum pixel values and validating them using sum thresholds, ensuring accurate detection of black borders and their boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior art techniques are used to detect black borders, then the detection process can be performed, but the accuracy of boundary determination is insufficient
Solution Approach 1:
The patent segments the black border detection process into two distinct phases: candidate identification using maximum grey scale values, and validation using sum of grey scale values. This segmentation allows each phase to focus on specific aspects of detection, improving overall boundary determination accuracy while maintaining reliable detection results.
Solution Approach 2:
The patent introduces an intermediary validation step using sum of grey scale values that acts as a mediator between raw pixel data and final border determination. This intermediary mechanism filters false candidates and confirms true black borders, resolving the contradiction between measurement precision and reliability.
2Productivity
If simple detection methods are used, then the processing speed is fast, but the detection accuracy is insufficient
Solution Approach 1:
The patent performs preliminary action by first identifying candidate black borders using maximum grey scale values before conducting the more computationally intensive validation step. This preliminary filtering reduces the number of candidates requiring detailed analysis, maintaining detection speed while improving accuracy through the two-stage approach.
Solution Approach 2:
The patent applies partial action by using maximum grey scale values for initial candidate identification (a simpler, faster operation) and then applying sum validation only to these candidates rather than all pixels. This partial application of the more rigorous validation method maintains productivity while achieving sufficient detection accuracy.
Data Source
AI summary
Black borders are detected in an image frame using a grey scale image of the image frame, and an edge image of the image frame. Candidate black borders are identified using maximum grey scale values associated with rows and columns of pixels of the grey scale image of the image frame, and then validated using a sum of grey scale values associated with rows and columns of pixels in the edge image of the image frame. If the validation fails, it is presumed that no black border exists.