Tiered Tolerance Video Verification System
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Solution Overview
Problem
Existing video verification methods are limited by hardware constraints, fail to detect subtle differences such as color tone and pixel-level details, and produce frequent false negatives due to inadequate sampling techniques, which are not effective in verifying content like titles and credits or transitions between clips.
Innovation Solution
A video verification system that compares sample images to reference images using a tiered tolerance mechanism at bitmap, pixel, and color channel levels, with intelligent sampling techniques like pseudo-random and variable sampling frequency to reduce false negatives and improve detection of subtle differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If every frame of a subject video clip is compared to frames from a known reference video clip, then verification precision is improved, but computational complexity and hardware requirements increase
Solution Approach 1:
The patent segments the video verification process into three distinct levels: bitmap-level comparison (overall image difference), pixel-level comparison (individual pixel differences), and color channel-level comparison (subtle color tone differences). This segmentation allows the system to perform comprehensive verification without processing every single pixel of every frame simultaneously, thereby reducing computational complexity while maintaining precision.
Solution Approach 2:
The patent applies partial action by comparing only a subset of pixels at each level rather than all pixels. At the bitmap level, it compares overall image characteristics; at the pixel level, it samples specific pixels; and at the color channel level, it analyzes color tone differences. This partial comparison approach reduces the total computational load while still detecting significant differences.
2Device complexity
If existing frame comparison techniques are used, then hardware requirements are reduced, but detection precision for subtle differences deteriorates
Solution Approach 1:
The patent introduces additional dimensions of comparison beyond simple pixel matching. It compares images at three hierarchical levels: bitmap level (overall structure), pixel level (individual pixel values), and color channel level (color tone and shading differences). This multi-dimensional approach enables detection of subtle differences that single-level comparison methods miss, while maintaining reasonable hardware requirements through intelligent sampling.
Solution Approach 2:
The patent changes the parameters of comparison by introducing tolerance thresholds at each level. Instead of requiring exact pixel matches, it allows for specified tolerances in color channel differences and pixel variations. This parameter adjustment enables the system to detect subtle perceptible differences while reducing the stringency of hardware requirements compared to exact matching methods.
3Ease of operation
If equal spacing sampling technique is used, then sampling simplicity is improved, but verification effectiveness deteriorates
Solution Approach 1:
The patent transitions from static equal-spacing sampling to dynamic sampling that adapts to video content characteristics. It employs variable sampling frequencies based on video duration, content type (e.g., titles, credits, transitions), and temporal distribution. This dynamic sampling strategy ensures effective verification of all content types while maintaining operational simplicity through automated adaptation.
Solution Approach 2:
The patent performs preliminary analysis of video metadata and content characteristics before sampling begins. It identifies key segments such as titles, credits, and transitions based on video properties, then pre-determines optimal sampling points for these segments. This preliminary action ensures that critical content is sampled effectively without requiring complex real-time analysis during the verification process.
4Device complexity
If existing sampling techniques are used, then sampling process is simplified, but false negative rate increases
Solution Approach 1:
The patent implements feedback mechanisms at each comparison level where results from bitmap-level, pixel-level, and color channel-level comparisons are integrated. This multi-level feedback system allows the verification process to detect differences that might be missed at individual levels, thereby reducing false negatives while keeping the sampling process relatively simple through automated multi-level analysis.
Solution Approach 2:
The patent performs preliminary identification of critical video segments (titles, credits, transitions) before sampling. By pre-determining which segments require focused sampling based on video metadata and content analysis, the system ensures comprehensive coverage of potentially problematic areas without requiring overly complex sampling procedures, thus reducing false negatives while maintaining process simplicity.
Data Source
AI summary
Comparing a sample image to a reference image. Differences between the color channel values of the pixels in the sample image and the corresponding color channel values for the corresponding pixels in the reference image are calculated and compared to predefined tolerances. Based on the comparisons, a pixel status for the pixels in the sample image is defined. An image status indicating whether the sample image differs from the reference image is defined based on the defined pixel status.