Content-Aware Background Generation for Foreground Visibility
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Solution Overview
Problem
Conventional methods for generating digital image backgrounds are time-consuming, error-prone, and lack user control, often resulting in visually unpleasing and outdated designs that interfere with foreground objects.
Innovation Solution
A content-aware background generation system using a machine-learning model, such as a compositional pattern producing neural network (CPPN), generates backgrounds based on foreground objects, allowing user control through parameters like variance, color theme, and opacity, and supports dynamic adjustments to maintain visual harmony.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual photo editing tools are used to adjust layout context, then background can be harmonized with foreground objects, but the process becomes time-consuming and frustrating
Solution Approach 1:
The patent replaces manual mechanical photo editing operations with an automated machine learning system that uses neural networks to generate and adjust backgrounds. The system automatically analyzes foreground objects, generates appropriate backgrounds, and adjusts layout context without requiring manual intervention, thereby eliminating the time-consuming nature of manual editing while maintaining high quality harmonization results.
Solution Approach 2:
The background generation system performs self-adjustment by automatically analyzing the digital image, identifying foreground objects, and generating backgrounds that are inherently harmonized with the content. The system serves itself by autonomously making design decisions about color schemes, patterns, and layout adjustments based on the analyzed foreground elements, eliminating the need for iterative manual refinement.
2Measurement precision
If opaque text boxes are placed behind text to maintain visibility, then foreground object readability is improved, but visual interference with overall design increases
Solution Approach 1:
The patent applies local quality by generating backgrounds with spatially varying properties - different regions of the background have different opacities, colors, and patterns tailored to local requirements. The system analyzes the foreground object positions and adjusts background characteristics in specific areas to ensure readability where needed while maintaining aesthetic quality in other regions, avoiding the need for blanket opaque text boxes that interfere with overall design.
Solution Approach 2:
The background generation system creates dynamic, adaptive backgrounds that automatically adjust their properties based on the foreground content. The system dynamically modifies background opacity, color intensity, and pattern density in response to the presence and characteristics of foreground objects, ensuring optimal readability without requiring static, visually intrusive text boxes. This dynamic adaptation allows the background to serve multiple functions simultaneously.
3Ease of manufacture
If conventional background generation methods are used, then simple backgrounds can be created, but user control and visual richness are limited
Solution Approach 1:
The patent implements a universal background generation system that can produce multiple types of backgrounds (solid colors, gradients, patterns, abstract designs) through a single machine learning model. The system is multi-functional, capable of adapting to different user requirements, foreground types, and design styles without requiring separate tools or complex procedures. Users gain control through configurable parameters while the system maintains simplicity of operation through automated processing.
Solution Approach 2:
The system enables user control through configurable parameters such as background type, color scheme, pattern density, and opacity levels. By allowing users to adjust these parameters, the system provides versatility and visual richness while maintaining ease of manufacture - users can simply modify parameters rather than manually design backgrounds. The machine learning model automatically processes these parameter changes to generate appropriate backgrounds, bridging simplicity and control.
Data Source
AI summary
Content aware background generation techniques are described. In one or more examples, a background generation system forms a mask from a digital image and receives an input specifying one or more parameters. The background generation system then generates a background using a machine-learning model and generative artificial intelligence by predicting pixel values based on the digital image, the one or more parameters, and the mask using a loss function. The background is then applied to the digital image and presented for display in a user interface.