AI Image Capture Guidance Using Contextual Cues and Editing Prompts
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Solution Overview
Problem
Individuals lacking photography expertise find it challenging to capture high-quality images due to difficulties in setting optimal camera parameters, and existing technologies lack effective real-time guidance and feedback for improving image quality.
Innovation Solution
An intelligent image capture guidance system using AI to analyze contextual cues from the field of view, provide real-time feedback, and perform post-processing to enhance image quality through generative AI models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If individuals attempt to capture high-quality images without photography expertise, then they can use the device easily, but the image quality and aesthetic results deteriorate due to inability to set optimal camera parameters
Solution Approach 1:
The system performs self-service by automatically analyzing the captured image, identifying target objects, extracting contextual cues, and generating editing prompts without requiring user expertise. The AI model autonomously determines optimal post-processing parameters and applies edits to achieve high-quality aesthetic results while the user simply captures the raw image.
Solution Approach 2:
The patent replaces the mechanical/manual system of manual camera parameter setting and manual image editing with an AI-based automated system. The visual-language model and generative AI model substitute for the photographer's expertise, automatically analyzing image content and applying appropriate aesthetic rules to produce high-quality results without requiring the user to understand photography techniques.
2Manufacturing precision
If existing technologies provide manual image editing tools, then users have control over editing, but the process becomes complex and time-consuming for achieving aesthetic results
Solution Approach 1:
The system implements feedback by having the AI model analyze the captured image, identify target objects, extract contextual cues about the scene and subjects, and use this information to generate appropriate editing prompts. The system continuously refines the editing process by comparing the original image with aesthetic rules and adjusting parameters to achieve optimal aesthetic quality while maintaining simplicity for the user.
Solution Approach 2:
The AI-based editing system provides universal functionality by automatically applying appropriate aesthetic rules across different image types, subjects, and scenarios. A single automated system handles diverse editing needs (portraits, landscapes, products, etc.) by analyzing contextual cues and selecting relevant aesthetic rules, eliminating the need for multiple specialized editing tools or expert knowledge.
3Loss of information
If no real-time guidance is provided during image capture, then the system remains simple, but users lack feedback to improve their photography skills and image quality
Solution Approach 1:
The system performs preliminary action by analyzing the captured image immediately after capture, identifying target objects before editing, and extracting contextual cues that inform subsequent editing decisions. This preliminary analysis provides users with immediate feedback about what was captured and how it can be improved, helping them learn photography principles without adding significant complexity to the overall system.
Data Source
AI summary
A computing device obtains an image and detects at least one target object depicted in the image. The computing device applies a visual-language model (VLM) to extract the contextual cues from the image relating to the at least one target object. The computing device obtains an aesthetic rule describing a desired post-processing result and generates editing prompts based on the contextual cues and the aesthetic rule. The computing device performs post-processing on the image by the generative artificial intelligence model based on the editing prompts and outputs a modified image.