Pixel-Level Artifact Detection Using Synthetic Frame Refinement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional artifact detection systems rely heavily on manual inspection, which is time-consuming and prone to human error, especially as image and video resolutions increase, leading to inefficiencies and potential misdiagnosis or costly rework.
Innovation Solution
A computer-implemented method for training a machine learning model to detect pixel-level artifacts using synthetic and refinement data, iteratively improving the model's accuracy through supervised learning and refinement data selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection is used for artifact detection, then detection capability is provided, but processing time increases and productivity decreases
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated machine learning-based detection system. The artifact detection model processes video frames automatically, substituting human operators with an algorithmic system that maintains detection capability while dramatically improving processing speed and productivity.
Solution Approach 2:
The system enables self-service artifact detection by training the machine learning model to autonomously identify artifacts without human intervention. The model learns from training data and independently performs detection tasks, eliminating the need for continuous manual inspection while maintaining reliable artifact identification.
2Ease of operation
If manual inspection is used for artifact detection, then detection capability is provided, but human error increases and reliability decreases
Solution Approach 1:
The patent replaces human manual inspection with an automated machine learning system to eliminate human error. The algorithmic detection process provides consistent, repeatable results without the variability and mistakes inherent in manual operations, thereby improving reliability while maintaining ease of operation.
Solution Approach 2:
The system incorporates feedback mechanisms through iterative training where detection results are used to refine and improve the model's accuracy. This continuous learning process enhances detection reliability by identifying and correcting errors, ensuring the system becomes increasingly accurate over time while remaining easy to operate.
3Manufacturing precision
If image and video resolutions increase, then quality improves, but data volume increases exponentially making manual inspection impractical
Solution Approach 1:
The patent replaces manual inspection with automated machine learning processing to handle high-resolution video data. The system can process 4K, 8K, and higher resolutions efficiently without the exponential time increase that would affect manual inspection, maintaining image quality assessment capability while eliminating time loss.
Solution Approach 2:
The system dynamically adapts to varying resolutions and data volumes through scalable machine learning processing. The artifact detection model can adjust its processing capacity to handle different video resolutions efficiently, maintaining consistent performance whether processing standard definition or ultra-high definition content without proportional increases in inspection time.
4Reliability
If manual inspection is used, then detection capability is provided, but scalability is limited and cannot handle growing data volumes
Solution Approach 1:
The patent replaces manual inspection with scalable automated processing using machine learning. The system can handle growing data volumes and increasing video resolutions without the limitations of human capacity, providing both reliable artifact detection and the adaptability to scale with increasing data demands.
Solution Approach 2:
The artifact detection model is designed with universal applicability to handle various video resolutions, formats, and types of artifacts. This multi-functional capability allows the system to scale across different applications and data volumes while maintaining consistent detection reliability, adapting to growing data requirements without requiring separate manual processes.
Data Source
AI summary
Techniques for generating for training a machine learning model to detect image artifacts include training, based on a first plurality of video frames having synthetic artifacts, a machine learning model to generate a trained machine learning model, generating, based on a second plurality of video frames, a plurality of first artifact detections using the trained machine learning model, selecting, from the second plurality of video frames based on the plurality of first artifact detections, to generate refinement data, and re-training, based on the first plurality of video frames and the refinement data, the trained machine learning model to detect image artifacts in video frames.