Auto-complete Image Suggestions Using Adaptive Histogram Descriptors
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Solution Overview
Problem
Existing image editing systems are inefficient and tedious, as users must manually adjust multiple aspects of an image, and conventional automation methods override user edits with constant presets, leading to unsuitable results.
Innovation Solution
The generation of auto-complete image suggestions using an image-editing index with adaptive-histogram descriptors to identify similar images and apply suitable edits, allowing user edits to be maintained and adapted for each image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual image editing is performed, then editing precision can be controlled, but time consumption increases significantly
Solution Approach 1:
The system pre-processes a dataset of images during an indexing phase, organizing them by visual similarity and edit characteristics before actual editing occurs. This preliminary organization enables rapid retrieval of relevant edit suggestions during the editing process, reducing real-time computation while maintaining precision.
Solution Approach 2:
The system introduces an intermediary layer between manual editing and automated presets. This intermediary analyzes the user's current edits and image characteristics to generate contextualized suggestions, bridging the gap between rigid presets and fully manual editing, thereby reducing time consumption while preserving editing precision.
2Productivity
If conventional automated presets are applied, then editing efficiency improves, but adaptability to user needs and image content deteriorates
Solution Approach 1:
The system transitions from static presets to dynamic, context-aware suggestions. The suggestion generation adapts in real-time based on the user's current edits, the specific image content, and the organized dataset, allowing the system to respond flexibly to different editing scenarios while maintaining high efficiency.
Solution Approach 2:
The system applies different levels of automation to different aspects of image editing based on user preferences and image characteristics. Rather than applying a uniform preset, the system generates targeted suggestions for specific edit types, preserving user control over certain aspects while automating others, thereby improving both efficiency and adaptability.
3Device complexity
If constant edit settings are used across all images, then system complexity is reduced, but manufacturing precision of edits deteriorates
Solution Approach 1:
The system segments the editing process into distinct phases: a pre-computation indexing phase that organizes images and edits by characteristics, and a runtime suggestion generation phase that retrieves and adapts edits based on the current image and user preferences. This segmentation allows complex, adaptive editing without requiring complex real-time processing.
Data Source
AI summary
Methods and systems are provided for generating auto-complete image suggestions. In embodiments described herein, a user image having an edit state is obtained. An edit state can indicate any edits applied by the user to the user image. For the user image, an auto-complete image suggestion is generated. The auto-complete image suggestion includes a representation of the user image with the user-applied edits as well as a set of supplemental edits. Such supplemental edits can be determined from a pre-edited image identified as similar to the user image.