Digital Image Clutter Estimation Using Inequality Index
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Solution Overview
Problem
Current methods for estimating the perceived clutter of digital images in the consumer domain are inadequate, relying on low-level image information and failing to accurately predict image quality.
Innovation Solution
A method that analyzes digital images using a data processor to determine a set of reference features, associate them with image features, form frequency distributions, calculate an inequality index, and assess scene content features to estimate clutter, mimicking human visual perception and incorporating higher-level image content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If low-level image information (color, orientation, luminance contrast) is used to predict image clutter, then the prediction method is simple and computationally efficient, but the prediction accuracy is limited and insufficient for consumer image domain
Solution Approach 1:
The patent segments the clutter estimation process into multiple independent modules: form frequency representation module, inequality index computation module, and scene content feature module. Each module processes specific aspects of image analysis separately, allowing for improved accuracy through comprehensive feature analysis while maintaining computational efficiency through modular design. This segmentation enables the system to handle complex high-level features without overwhelming computational burden.
Solution Approach 2:
The patent transitions from analyzing only low-level image features to incorporating high-level semantic content and spatial structures. By adding the dimension of scene content understanding (detecting objects, people, text, spatial relationships), the system achieves more accurate clutter prediction that reflects human visual perception. This dimensional expansion allows the model to capture meaningful image content beyond basic color and contrast information.
2Reliability
If traditional clutter estimation methods are used, then the processing speed is fast, but the results do not closely model human visual perception and fail to capture semantic content
Solution Approach 1:
The patent performs form frequency representation and inequality index computation as preliminary processing steps before final clutter estimation. These pre-computed features capture the statistical distribution of visual elements and their spatial arrangements, providing a foundation for accurate perceptual assessment. By preparing these features in advance, the system reduces the computational burden during final clutter calculation, balancing accuracy with processing efficiency.
Solution Approach 2:
The patent changes the parameters being analyzed from simple low-level features to comprehensive high-level scene content features. By detecting and analyzing semantic elements (objects, people, text), spatial structures, and their relationships, the system achieves results that closely model human visual perception. The inequality index parameter captures the statistical variation of visual elements, providing a reliable measure of perceptual clutter that reflects how humans actually perceive image complexity.
Data Source
AI summary
A method for determining an estimated clutter level of an input digital image based on an inequality index. The inequality index is determined by analyzing the input digital image to determine a set of image features. The image features are associated with a set of designated reference features, and the inequality index is determined based on the statistical variation of the reference features. A set of scene content features relating to spatial structures or semantic content of the input digital image is determined by analyzing the input digital image. The estimated clutter is determined responsive to the inequality index and the scene content features.