Video Waveform Peak Detection for HDR Out-of-Range Pixels
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Solution Overview
Problem
Existing waveform monitors struggle to efficiently determine transient peak values in high dynamic range (HDR) videos, requiring manual frame-by-frame analysis to identify out-of-range pixels, which is time-consuming and inaccurate.
Innovation Solution
A waveform monitor device that automatically alerts users to out-of-band signals by displaying color-coded bars for each component signal, allowing quick identification of frames with out-of-range pixels, and supports both HDR and standard dynamic range (SDR) color spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual frame-by-frame analysis is used to detect peak values, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The system performs preliminary analysis by continuously monitoring video signals and pre-identifying frames containing peak values that exceed thresholds. This preliminary detection allows the system to prepare and immediately display alerts when peaks occur, eliminating the need for time-consuming manual frame-by-frame review while maintaining accurate detection of transient peak values.
2Productivity
If automated peak detection is implemented, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system employs feedback mechanisms where detected peak values are continuously compared against established thresholds, and detection parameters are adjusted based on the characteristics of the video signal. This feedback loop ensures that automated detection maintains high precision by adapting to different video content and peak patterns while operating at full automated speed.
3Measurement precision
If frame-by-frame manual review is performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system introduces an intermediary automated detection layer that processes video frames and identifies peak values before presentation to the user. This intermediary system filters and flags frames containing out-of-range pixels, allowing users to quickly review only relevant frames rather than manually examining every frame, thus reducing time loss while preserving identification accuracy.
4Measurement precision
If continuous monitoring of all video frames is performed, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system applies partial monitoring by focusing computational resources only on detecting peak values that exceed predetermined thresholds rather than analyzing every pixel in every frame. This selective approach maintains detection reliability for transient peaks while significantly reducing the overall processing load and energy consumption by ignoring frames and pixels that do not contain peak values.
Data Source
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AI summary
A waveform monitor device, or media analysis device, to monitor a video signal. The waveform monitor device may include an input to receive the video signal, the video signal having a plurality of frames, a memory to store the received video signal, a processor coupled to the memory, and a display. The processor separates the video signal into at least two component signals, for at least one component signal, determines a peak value of the at least one component signal for at least one frame of the plurality of frames, generates a marker at the peak value, determines if the peak value violates a predetermined threshold, and generates an alert when the peak value violates the predetermined threshold. The at least one component signal, the marker at the peak value on the component signal and the alert when the peak value violates the predetermined threshold are displayed on the display to allow a user to quickly determine if a video signal is within a required threshold.