Automated Banner Image Layout for Scalable Media Promotion
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Solution Overview
Problem
Conventional manual generation of banner images for content programs is time-consuming and impractical for large volumes, as it requires significant human crafting and artistic judgment, making it inefficient for content providers to generate banner images for numerous content programs.
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, to efficiently produce banner images for a wide range of content programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual generation of banner images is used, then quality and artistic judgment are maintained, but time consumption and labor resources 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 separate analytical models that can be independently optimized and executed, enabling automated high-volume processing while maintaining quality standards.
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 capability eliminates the need for manual artistic judgment while maintaining consistent quality across large volumes of banner images.
2Productivity
If automated analytical models are used, then processing speed and volume increase, but system complexity increases
Solution Approach 1:
The complex automated system is organized into modular analytical models for each generation phase (image selection, text placement, segmentation, font selection, effects). This segmentation reduces system complexity by allowing independent development, testing, and maintenance of each component while achieving high overall productivity.
3Adaptability or versatility
If manual crafting is used for each banner, then customization and quality control are maintained, but scalability to large volumes is limited
Solution Approach 1:
The analytical models automatically adapt to different content programs by selecting appropriate images, fonts, and effects based on program characteristics. This self-service capability provides scalability to large volumes while maintaining customization through algorithmic decision-making that adjusts to each specific case.
Solution Approach 2:
The system adjusts various parameters (image selection criteria, text placement coordinates, font choices, effect intensities) based on the specific content program being processed. This parameter adaptation enables scalable production of customized banners for different programs without manual intervention.
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.