Personalized Image Capture Subject Modeling
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Solution Overview
Problem
Image capture operations in consumer electronic devices often result in unnatural image distortions due to improper application of enhancements, leading to degradation in perceived image quality, especially when capturing human subjects.
Innovation Solution
The system personalizes image capture operations by detecting a subject's face, generating models from multiple images, and applying adjustment parameters to ensure tailored image processing, avoiding distortions through face extraction, modeling, and evaluation units that compare and adjust image characteristics like brightness, color, and resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image enhancements are applied to improve perceived quality, then image quality can be improved, but color distortions and aberrations occur causing unnatural appearance
Solution Approach 1:
The system performs preliminary actions by detecting the subject's face and generating a unique subject model before applying image enhancements. This pre-processing step establishes baseline characteristics of the subject's skin tone, facial features, and appearance, allowing subsequent enhancement operations to be tailored specifically to that subject rather than applying generic filters that cause color distortions and unnatural appearances.
Solution Approach 2:
The system applies local quality by generating subject-specific adjustment parameters that are tailored to individual characteristics of each detected subject. Instead of applying uniform enhancement parameters across all images, the system creates customized parameters based on the subject's unique features, ensuring that enhancements improve image quality without causing color distortions or aberrations specific to that subject's appearance.
2Productivity
If generic image enhancement parameters are used, then processing is simple and fast, but images appear unnatural with distortions
Solution Approach 1:
The system implements parameter changes by dynamically generating subject-specific enhancement parameters based on detected facial features and characteristics. The evaluation unit compares the subject model against ideal or reference models and adjusts parameters such as brightness, contrast, color balance, and sharpness specifically for each subject. This approach maintains processing efficiency while eliminating the unnatural appearances caused by generic enhancement parameters.
Solution Approach 2:
The system performs preliminary subject detection and model generation before applying enhancements, which enables subsequent processing to use optimized, pre-calculated parameters specific to each subject. This preliminary characterization allows the system to maintain high processing speed while achieving natural-looking results, as the subject-specific parameters are prepared in advance rather than requiring complex real-time adjustments.
3Manufacturing precision
If subject-specific modeling is implemented, then image adjustments are tailored and natural, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the image processing task into distinct functional units: a detection unit that identifies and isolates the subject's face, a modeling unit that generates subject-specific characteristics, and an evaluation unit that creates tailored enhancement parameters. This modular segmentation allows each component to specialize in a specific function, achieving natural-looking subject-specific adjustments while managing system complexity through organized, independent modules rather than a monolithic complex system.
Solution Approach 2:
The system uses an intermediary approach by introducing a subject model as an intermediate representation between the raw image input and the enhancement application. The detection unit extracts facial features and creates this intermediate subject model, which then serves as the basis for the evaluation unit to generate appropriate enhancement parameters. This intermediary subject model simplifies the overall process by providing a structured intermediate form that bridges detection and enhancement, reducing complexity compared to direct pixel-level manipulation.
Data Source
AI summary
Methods and apparatuses are disclosed to personalize image capture operations of imaging equipment according to models that correspond uniquely to subjects being imaged. According to these techniques, a subject's face may be detected from a first image supplied by an image source and a first model of the subject may be developed from the detected face. The first model of the subject may be compared to another model of the subject developed from other images. Image adjustment parameters may be generated from a comparison of these models, which may control image adjustment techniques that are applied to the newly captured image of the subject. In this manner, aspects of the present disclosure may generate image capture operations that are tailored to characteristics of the subjects being imaged and avoid artifacts that otherwise could cause image degradation.