Image Quality Assessment Without Reference Copy
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Solution Overview
Problem
Existing methods for assessing image quality, such as Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE), require a reference copy, which is not always available, especially in consumer digital imaging applications like browsing and managing large image databases, making objective image quality assessment challenging without a comparative reference.
Innovation Solution
The system detects target object regions in images, generates image quality feature vectors, and maps these vectors to quantitative measures of image quality, using modules for target object detection, feature extraction, and image quality assessment, decoupling object detection from image assessment to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reference-based objective image quality assessment methods (PSNR, MSE) are used, then measurement precision is improved, but device complexity increases due to requirement of reference copy
Solution Approach 1:
The patent extracts the essential features from the input image itself (target object region, edge information, texture characteristics) without requiring an external reference copy. The feature extraction module isolates and analyzes these intrinsic image properties to generate quality assessment results independently.
Solution Approach 2:
The image quality assessment system performs self-assessment by analyzing its own input image characteristics. The system uses the input image's inherent features (through target object detection, edge detection, and texture analysis) to evaluate its own quality without needing external reference material.
2Measurement precision
If person-based subjective image quality assessment methods are used, then measurement precision is improved, but productivity deteriorates due to time consumption and cost
Solution Approach 1:
The patent replaces the mechanical process of human subjective assessment with an automated computational system. The system uses image processing algorithms (target object detection, edge detection, texture analysis) to automatically evaluate image quality, eliminating the need for human evaluators while maintaining objective and consistent measurements.
3Measurement precision
If reference-based objective assessment is used, then measurement precision is improved, but ease of operation deteriorates when reference is not available
Solution Approach 1:
The system enables self-service quality assessment where the input image itself provides all necessary information for evaluation. The feature extraction module processes the input image's intrinsic characteristics (target objects, edges, textures) to generate quality metrics without requiring external reference material or additional input.
Data Source
AI summary
Systems and methods of assessing image quality are described. In one scheme for assessing image quality, a target object region is detected in an input image. An image quality feature vector representing the target object region in an image quality feature space is generated. The image quality feature vector is mapped to a measure of image quality. In one scheme for generating an image quality assessment engine, target object regions are detected in multiple input images. Image quality feature vectors representing the target object regions in an image quality feature space are generated. The image quality feature vectors are correlated with respective measures of image quality assigned to the input images. A mapping between image quality feature vectors and assigned measures of image quality is computed.