Frame-Differenced Avatar Replacement for Sporadic Face Pixelation
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Solution Overview
Problem
Existing technologies struggle to effectively detect and mitigate sporadic video artifacts, particularly those affecting human faces, due to their perceptual nature and the limitations of conventional error correction mechanisms, which are not suitable for identifying and correcting these distortions.
Innovation Solution
A computing device employs a two-stage solution involving enhanced frame differencing techniques and machine learning to detect video artifacts, followed by replacing distorted faces with avatars, using a pixel/frame subtractor, AI/ML detector, avatar generator, and compositor to create a modified video stream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional error correction mechanisms are used to mitigate video artifacts, then the system complexity remains low, but the detection and mitigation effectiveness of sporadic pixelation artifacts is insufficient
Solution Approach 1:
The patent introduces an intermediary avatar representation layer between the original video signal and the final output. When pixelation artifacts are detected in human faces, the system replaces the distorted facial regions with avatar representations that capture the essential identity features. This intermediary approach allows for effective artifact mitigation without requiring complex direct correction algorithms, as the avatar serves as a mediator that bridges the original content and the corrected output.
Solution Approach 2:
The system creates a copy of the original video stream and processes only the affected regions (pixelated face areas) by replacing them with avatar representations. This copying approach allows the system to maintain the original video quality in non-affected regions while applying corrective measures only where necessary, thereby improving reliability without proportionally increasing overall system complexity.
2Productivity
If advanced video coding standards like HEVC and VVC are used to improve compression efficiency, then data storage and transmission efficiency is improved, but bandwidth consumption increases due to new applications like 4K/8K video and virtual reality
Solution Approach 1:
The patent applies local quality enhancement by targeting only the specific regions where pixelation artifacts occur (primarily human faces) for avatar replacement, rather than reprocessing the entire video stream. This localized approach maintains high compression efficiency from advanced coding standards while providing quality improvement only where needed, effectively managing the bandwidth-quality tradeoff.
Solution Approach 2:
The system performs preliminary detection of pixelation artifacts using frame differencing and shape analysis before the actual avatar replacement occurs. This preliminary action identifies the exact regions that need correction, allowing the system to prepare and apply avatar replacements efficiently without unnecessary processing of the entire video stream, thus optimizing bandwidth utilization.
3Manufacturing precision
If real-time avatar generation and integration is performed to mitigate video artifacts, then image quality is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by processing only the minimal necessary regions (pixelated face areas) rather than the entire video frame. The avatar generation and integration processes are applied selectively to only those regions where artifacts are detected, significantly reducing processing time and computational resources while maintaining high image quality in the affected regions.
Solution Approach 2:
The patent implements a streamlined pipeline that skips unnecessary processing steps. After detecting pixelation artifacts through frame differencing and shape analysis, the system directly proceeds to avatar replacement without intermediate processing stages. This rushing through of the processing pipeline minimizes delays and computational overhead while delivering real-time image quality improvement.
Data Source
AI summary
Systems and methods for detecting and/or mitigating video artifacts in a video. A processor in a computing device may be configured to receive the video from a video source (e.g., codec or from any other video source compressed or uncompressed) subtract pixel intensities from two consecutive frames in the video to generate a residual, analyze the residual to detect small rectangular shapes, and identifying a video artifact in the video based on a result of the analysis.