Image Data Analysis Using Weighted Sample Ranges for Constant Frame Detection
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Solution Overview
Problem
Current methods for monitoring and analyzing film or video content are personnel-intensive and lack automated or semi-automated error detection capabilities, particularly for identifying issues like all-black frames, which can lead to false alarms and reduced reliability.
Innovation Solution
A method and apparatus for analyzing sampled or sub-sampled image data by defining sample value ranges with signed weights, forming a weighted sum, and comparing it to a threshold to determine if image data sample values are close to a defined value, such as black or white levels, using a discriminator function to accurately detect constant luminance or color frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated detection methods are implemented, then productivity and reliability improve, but device complexity increases
Solution Approach 1:
The image data analysis is segmented into multiple sample value ranges (e.g., black level range, white level range, mid-grey range) with different weights. This allows the complex detection task to be divided into simpler, weighted comparisons across discrete ranges, improving automated detection while managing computational complexity.
Solution Approach 2:
The system changes parameters by assigning different signed weights to different sample value ranges and adjusting threshold values. This parameter-based approach enables flexible automated detection of constant luminance frames without requiring complex algorithms, resolving the contradiction between automation and complexity.
2Device complexity
If simple threshold comparison is used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
Instead of a single threshold comparison, the luminance range is segmented into multiple sample value ranges (black, white, mid-grey, etc.), each with specific weights. This segmentation maintains algorithm simplicity while improving precision by accounting for different luminance regions separately.
Solution Approach 2:
Different weights are assigned to different sample value ranges based on their local characteristics. Critical ranges like black level have different weighting than mid-grey ranges, allowing precision-critical regions to be detected with higher accuracy while keeping the overall system simple.
3Measurement precision
If visual monitoring by personnel is used, then measurement precision is maintained, but productivity and reliability worsen due to human error and personnel intensity
Solution Approach 1:
The system performs self-service automated monitoring by automatically analyzing image data, generating metadata, and detecting constant luminance frames without human intervention. This eliminates personnel-intensive visual monitoring while maintaining detection accuracy through the weighted sample value range methodology.
Solution Approach 2:
The patent replaces the mechanical human visual monitoring system with an automated electronic analysis system that processes image data through weighted sample value range comparisons. This substitution maintains precision while dramatically improving productivity and eliminating human error.
Data Source
AI summary
A method for the automatic detection of fields or frames in film or video content having similar or substantially the same luminance or color component values, for example to detect black frames. Image sample values are assigned to a sample value range having a signed weight associated therewith, and a contribution to a discriminator function is determined for each sample range depending on the signed weight and the number of input samples in the sample value range. The discriminator function output is then compared with a threshold.