Neural Network Image Quality Scorer for Automated Visual Assessment
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Solution Overview
Problem
Current systems lack an efficient method for automatically assessing the visual quality of media content, such as images, which is essential for applications like identity verification, where manual evaluation is time-consuming and resource-intensive.
Innovation Solution
A neural network-based image quality scorer machine that analyzes candidate images by predicting a similarity score based on trained image features, allowing for automated visual quality assessment and generation of a visual quality score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of image quality is performed, then measurement precision can be achieved, but productivity is reduced and time consumption increases
Solution Approach 1:
The patent replaces manual mechanical evaluation with an automated neural network-based image quality scorer machine. The system uses a trained neural network model that automatically analyzes image features and generates quality scores, eliminating the need for human operators while maintaining assessment accuracy through sophisticated algorithmic processing.
Solution Approach 2:
The image quality scorer machine performs self-service by automatically assessing image quality without external human intervention. The neural network model independently processes images, detects features, and generates quality scores based on pre-trained criteria, enabling the system to serve itself and eliminate dependency on manual evaluation resources.
2Measurement precision
If manual evaluation of image quality is performed, then measurement precision can be achieved, but loss of time increases
Solution Approach 1:
The patent replaces time-consuming manual evaluation with automated neural network processing. The system rapidly analyzes image features and generates quality scores through algorithmic computation, reducing evaluation time from minutes or hours of manual work to seconds of automated processing while preserving assessment precision.
Solution Approach 2:
The neural network model is pre-trained on extensive image datasets before deployment, allowing it to instantly recognize quality patterns without requiring real-time human expertise. This preliminary training action enables the system to perform rapid, accurate assessments by applying pre-learned knowledge to new images.
3Productivity
If automated image quality assessment is implemented using traditional methods, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent replaces traditional automated methods with a neural network-based system that combines automated processing speed with advanced pattern recognition. The neural network's ability to detect subtle image features and learn complex quality patterns enables high-speed processing without sacrificing assessment accuracy.
Solution Approach 2:
The system changes the operational parameters of automated assessment by using a trained neural network model with multiple detection layers. This transforms the processing approach from simple rule-based automation to sophisticated algorithmic analysis, maintaining high productivity while significantly improving measurement precision through advanced computational methods.
Data Source
AI summary
An image quality scorer machine accesses a candidate image to be analyzed for visual quality. The image quality scorer machine generates a visual quality score of the candidate image by first generating a prediction of a similarity score for the candidate image. The predicted similarly score of the candidate image may be generated by a process including inputting the candidate image into a neural network that has been trained to detect a set of image features in the candidate image and then to generate a corresponding predicted similarity score based on degrees to which the image features in the set are present in the candidate image. The image quality scorer machine derives the visual quality score based on the predicted similarity score outputted by the neural network. Accordingly, the image quality score machine may provide or store the generated visual quality score of candidate image for subsequent usage.