Neural Image Quality Detection for Low-Definition Filtering
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Solution Overview
Problem
Current image quality detection methods using preset indicators like peak signal-to-noise ratio or Fourier spectrum analysis often incorrectly classify low-definition images as high-quality, leading to erroneous task processing.
Innovation Solution
An image quality detection method that involves recognizing a target element in an input image, extracting a current region feature, comparing it with category features obtained from preset reference element images, and determining the image quality category based on matching degrees, using a neural network for feature extraction and fusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If preset image quality indicators (peak signal-to-noise ratio, Fourier spectrum analysis) are used for detection, then the detection process is simple and fast, but the classification accuracy deteriorates leading to incorrect identification of low-definition images as high-quality
Solution Approach 1:
The patent replaces traditional mechanical/mathematical image quality indicators (peak signal-to-noise ratio, Fourier spectrum analysis) with a neural network-based deep learning system. The neural network automatically learns complex image quality characteristics from training data, enabling accurate distinction between high and low definition images without relying on simple mathematical formulas that fail to capture semantic quality information.
Solution Approach 2:
The patent transforms the detection approach by changing from fixed mathematical parameters to learnable neural network parameters. The model automatically adjusts its internal parameters during training to optimize image quality classification, adapting to various image characteristics and quality metrics that traditional fixed indicators cannot capture.
2Ease of manufacture
If traditional image quality indicators are used, then the detection method is easy to implement, but the reliability of subsequent task processing deteriorates due to erroneous image classification
Solution Approach 1:
The patent substitutes simple mathematical calculations with a neural network model that has been trained on extensive image data. This replacement maintains implementation simplicity through a unified API interface while dramatically improving reliability by using deep learning to accurately assess image quality and filter out low-definition images before task processing.
Data Source
AI summary
An image quality detection method includes, obtaining an input image; recognizing a target element in an input image, and obtaining a target element region of the input image in which the target element is located; extracting a current region feature corresponding to the target element region; obtaining category features corresponding to preset image quality categories; comparing the current region feature with the category features, to obtain matching degrees between the current region feature and the category features, and determining a target category feature corresponding to the current region feature from the category features based on the matching degrees; and determining a target region quality category of the target element region based on a target preset image quality category corresponding to the target category feature, and outputting a predicted image quality category of the input image based on the target region quality category.