Automated Pixel Error Noticeability Prediction
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Solution Overview
Problem
Pixel errors in images are difficult and costly to correct due to their reliance on human inspectors, as not all pixel errors are of equal importance and require automated prediction of noticeability to determine their impact on the image's aesthetic.
Innovation Solution
An automated system using a neural network-based approach to predict the noticeability of pixel errors by analyzing images with a predictive model that includes local and global feature mapping branches, along with pixel parameter analysis, to determine distraction levels and confidence scores for each anomaly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human inspectors are used to correct pixel errors, then accuracy in identifying noticeable errors is improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent replaces the mechanical system of human inspection with an automated computational system consisting of pixel error detection algorithms and machine learning models. The system automatically detects pixel errors, extracts features, and predicts noticeability without human intervention, thereby maintaining accuracy while eliminating the cost and time associated with manual inspection.
Solution Approach 2:
The system enables self-service by allowing the image processing pipeline to automatically identify and prioritize pixel errors without requiring external human expertise. The automated noticeability prediction system serves itself by integrating detection, feature extraction, and prediction capabilities into a single autonomous workflow.
2Manufacturing precision
If all pixel errors are corrected, then image quality is improved, but resource waste occurs on insignificant errors
Solution Approach 1:
The patent applies local quality by differentiating between various types of pixel errors based on their visual impact and context. Instead of treating all errors uniformly, the system analyzes local image characteristics, error position, and visual properties to assign different priority levels. This allows selective correction of only those errors that significantly affect image quality, avoiding waste on insignificant errors.
Solution Approach 2:
The system changes parameters by transforming raw pixel error data into meaningful noticeability scores through feature extraction and machine learning. By converting multiple parameters (error type, position, local image characteristics) into a single predictive metric, the system enables prioritized correction based on actual visual impact rather than uniform treatment of all errors.
3Productivity
If automated systems are used to detect pixel errors, then productivity is improved, but measurement precision may deteriorate compared to human inspection
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on large datasets of pixel errors with known noticeability ratings. This preliminary training phase allows the automated system to learn human-like judgment criteria before deployment, ensuring that when the system operates in production, its predictions align closely with human inspector accuracy while maintaining automated efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms where prediction results can be compared against ground truth data or human inspector annotations during training and validation phases. This feedback loop allows continuous refinement of the model to improve measurement precision, ensuring the automated system converges toward or exceeds human-level accuracy in noticeability prediction.
Data Source
AI summary
A system includes a hardware processor and a memory storing a software code including a predictive model. The hardware processor executes the software code to receive an input including an image having a pixel anomaly, and image data identifying the location of the pixel anomaly in the image. The software code uses the predictive model to extract a global feature map of a global image region of the image, the pixel anomaly being located within the global image region; to extract a local feature map of a local image region of the image, the pixel anomaly being located within the local image region and the local image region being smaller than the global image region; and to predict, based on the global feature map and the local feature map, a distraction level of the pixel anomaly within the image.