Banner Image Layout Using AI Models for Text Placement
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Solution Overview
Problem
Conventional methods for generating banner images for content programs are time-consuming and impractical for large volumes, requiring significant human effort and artistic judgment, making it difficult for content providers to efficiently produce banner images for a wide range of content.
Innovation Solution
An automated system utilizing rule-based and machine learning models to break down the banner image generation process into discrete phases, including image selection, text placement, segmentation, font selection, and effects, enabling efficient and scalable production of banner images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used for generating banner images, then quality and artistic judgment are maintained, but time consumption and labor effort increase significantly
Solution Approach 1:
The banner image generation process is divided into discrete phases: image selection, text placement, segmentation, font selection, and effects application. Each phase is handled by a separate analytical model, enabling parallel processing and systematic automation while maintaining quality control at each step.
Solution Approach 2:
The system uses analytical models that automatically select images, determine text placement, choose fonts, and apply effects based on predefined criteria and machine learning algorithms. This self-service approach eliminates the need for manual artistic judgment while maintaining consistent quality across large volumes of banner images.
2Productivity
If automated analytical models are used for banner generation, then productivity increases and time is reduced, but system complexity increases
Solution Approach 1:
The complex automation task is broken down into manageable analytical models for each generation phase. This segmentation reduces the complexity of individual components while maintaining high overall productivity through coordinated operation of multiple specialized models.
Solution Approach 2:
The analytical models are designed to handle multiple aspects of banner generation (selection, placement, segmentation, font choice, effects) using unified machine learning frameworks. This multi-functionality reduces the need for separate specialized systems while maintaining flexibility and adaptability.
Data Source
AI summary
Example systems and methods for automated generation of banner images are disclosed. A program identifier associated with a particular media program may be received by a system, and used for accessing a set of iconic digital images and corresponding metadata associated with the particular media program. The system may select a particular iconic digital image for placing a banner of text associated with the particular media program, by applying an analytical model of banner-placement criteria to the iconic digital images. The system may apply another analytical model for banner generation to the particular iconic image to determine (i) dimensions and placement of a bounding box for containing the text, (ii) segmentation of the text for display within the bounding box, and (iii) selection of font, text size, and font color for display of the text. The system may store the particular iconic digital image and banner metadata specifying the banner.