Image Exposure Quality Detection via Face Region Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing technologies fail to effectively detect and correct exposure quality in digital images, particularly in varying ambient light conditions, leading to underexposed or overexposed images.
Innovation Solution
A method and system that compute an overall image exposure score and face region exposure scores based on clipped pixel and tonal distribution scores, adjusting these scores with a penalty value, and applying exposure corrections to automatically curate and correct images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If automatic image curation and correction is implemented, then image quality improvement is achieved, but measurement precision of exposure quality is insufficient
Solution Approach 1:
The patent segments the image into multiple regions including face regions and non-face regions, and computes separate exposure scores for each region. This segmentation allows for more precise measurement of exposure quality in different areas of the image, particularly ensuring that face regions are evaluated with appropriate weighting.
Solution Approach 2:
The patent applies local quality assessment by computing exposure scores for specific regions (face regions vs. non-face regions) rather than treating the entire image uniformly. Face regions are given special consideration with separate scoring and penalty adjustments, enabling localized quality measurement that reflects the importance of different image areas.
2Measurement precision
If exposure quality detection is enhanced with multiple scoring components, then measurement precision improves, but device complexity increases
Solution Approach 1:
The exposure score is segmented into distinct components: clipped pixel score, tonal distribution score, and penalty score. Each component measures a specific aspect of exposure quality, allowing for comprehensive assessment while maintaining clear separation of functions. This modular scoring approach improves measurement precision without creating excessive complexity.
Solution Approach 2:
The system applies local quality assessment by computing separate exposure scores for face regions and non-face regions, then combining them with appropriate weighting. This localized approach enhances measurement precision for critical areas while keeping the overall system manageable through region-specific processing.
3Manufacturing precision
If face region detection and separate scoring is implemented, then image quality assessment accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary face region detection and identification before computing the final exposure scores. By pre-identifying face regions and determining their boundaries, the system can efficiently apply region-specific scoring without redundant processing. This preliminary action reduces overall processing time while maintaining high assessment accuracy.
Solution Approach 2:
The system applies local quality assessment only to identified face regions rather than processing the entire image uniformly. This localized approach concentrates computational resources on critical areas, improving assessment accuracy for face regions while reducing unnecessary processing time for non-critical areas.
Data Source
AI summary
Systems, methods and computer readable media for exposure quality detection are described. In some implementations, a method can include computing an overall image exposure score for an image. The method can also include determining one or more face regions in the image. The method can further include computing a face region exposure score for each face region. The method can also include combining the overall image exposure score and each face region exposure score to generate an exposure quality score for the image.