Object-Based Image Quality Assessment Using Category Metrics
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Solution Overview
Problem
Conventional image quality detection mechanisms fail to account for differences in image quality among multiple objects within an image, assessing the quality of the entire image or video rather than individual objects.
Innovation Solution
An image processing system that performs object-based quality assessment by detecting and categorizing objects within an image, using object category metrics to evaluate the quality of each object and initiate actions for improvement or notification based on comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional global or local image quality detection mechanisms are used, then the assessment covers the entire image or a portion, but it fails to account for differences in image quality of different objects within the image
Solution Approach 1:
The patent segments the image into multiple objects and performs quality assessment on each object individually. The system detects objects within the image, determines object categories, and applies object-specific quality metrics to each detected object, enabling differentiated quality evaluation rather than treating the entire image as a single unit.
Solution Approach 2:
The patent implements local quality assessment by evaluating image quality metrics specifically for each detected object rather than applying uniform global metrics. Different object categories (e.g., face, text, natural scene) have different quality requirements, and the system applies appropriate quality standards locally to each object type, allowing precise quality control where needed.
2Measurement precision
If object-based quality assessment is implemented, then precise assessment of individual objects is achieved, but the processing complexity increases
Solution Approach 1:
The patent performs preliminary object detection and categorization before quality assessment. By first identifying objects and their categories within the image, the system prepares the groundwork for efficient quality evaluation. This preliminary segmentation and classification enables subsequent quality metrics to be applied directly to relevant object regions without redundant processing.
Solution Approach 2:
The patent employs a universal object detection and categorization framework that can identify multiple object types (faces, text, natural scenes, etc.) using a single system. This multi-functional approach allows the same detection infrastructure to serve various quality assessment needs across different object categories, reducing overall system complexity despite the diverse assessment requirements.
Data Source
AI summary
A method includes determining, at an image processing device, object quality values for a plurality of objects represented in an image. The object quality values are based on portions of image data for the image. The object quality values include a blurriness value for each object and a color value for each object. The method includes accessing, via the image processing device, object category metrics associated with an object category. The object category metrics include a blurriness metric for each object and a color metric for each object. The method also includes performing, with the image processing device, a particular image processing operation for the image based on comparisons of the object quality values for each object to corresponding object category metrics.