Dynamic Image Augmentation for Video Engagement
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Solution Overview
Problem
Current systems lack the ability to effectively enhance user engagement and comprehension of content by dynamically adjusting the presentation of objects within moving video images based on viewer interest levels and environmental factors, leading to suboptimal interaction with multimedia content.
Innovation Solution
A method and system that utilize object recognition, predictive modeling, and real-time feedback to selectively augment objects within moving video images, adjusting their presentation based on predicted viewer interest and environmental conditions, such as through color popping, brightening, or flashing, to maintain user attention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If objects in moving video images are presented in a static manner, then the system operation is simple, but user engagement and comprehension are reduced
Solution Approach 1:
The patent implements dynamic presentation of objects by continuously monitoring viewer location and interest levels, then adjusting object properties (brightness, size, position) in real-time. This transforms static video presentation into a dynamic adaptive system that responds to viewer state, thereby maintaining user engagement without complicating system operation through automated sensor integration and real-time processing.
Solution Approach 2:
The system changes multiple parameters of object presentation simultaneously based on viewer state: brightness enhancement when viewer attention is low, position adjustment to highlight important objects, and size modification to emphasize key elements. These parameter changes are driven by location data and interest level predictions, resolving the contradiction between operational simplicity and user engagement.
2Productivity
If the system dynamically adjusts object presentation based on viewer interest, then user engagement is enhanced, but device complexity increases
Solution Approach 1:
The system segments the video content into individual detectable objects, allowing independent processing and adjustment of each object's presentation parameters. This segmentation enables targeted augmentation of only those objects relevant to current viewer interest, rather than processing the entire video frame, thereby reducing computational complexity while maintaining engagement enhancement.
Solution Approach 2:
The system employs predictive modeling that automatically determines viewer interest levels based on location data and viewing patterns, eliminating the need for manual intervention or complex real-time analysis of viewer state. The predictive algorithm self-adjusts presentation parameters based on inferred interest levels, reducing system complexity while maintaining high user engagement.
3Productivity
If object recognition and predictive modeling are performed in real-time, then user engagement is maintained, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary object recognition and classification before full presentation, pre-identifying objects that may become relevant based on predicted viewer trajectories and interest patterns. This preliminary processing allows the system to have object lists ready in advance, reducing real-time processing requirements while maintaining continuous engagement through proactive content preparation.
Solution Approach 2:
The system applies enhanced processing only to specific regions or objects of interest rather than analyzing the entire video frame uniformly. By focusing computational resources on locally identified important objects based on viewer location and predicted interest, the system maintains real-time responsiveness without requiring full-frame processing of all video content.
4Loss of information
If the system augments multiple objects simultaneously, then content comprehension is enhanced, but information overload may occur
Solution Approach 1:
The system extracts and highlights only the most relevant objects for current viewer context, removing less important elements from active presentation. By selectively augmenting only those objects that align with predicted viewer interest and current location, the system enhances comprehension of key content while preventing information overload through deliberate exclusion of non-essential objects.
Solution Approach 2:
The system applies augmentation partially to objects based on their relevance score, rather than uniformly to all detected objects. By applying enhancement to only a subset of objects that exceed a relevance threshold, the system provides sufficient information for comprehension while avoiding the harmful effect of presenting too many augmented elements simultaneously.
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: obtaining image data of an image representation that is being displayed on one or more viewing device during an image representation presentation session, the image representation provided by a moving video image representation having successive frames of image data, wherein the image representation is being viewed by one or more viewer user; performing object recognition on at least one frame of image data of the successive frames of image data, and generating a list of object representations within the at least one frame of image data, the list of object representations including identifiers for respective object representations included within the image representation.