Image Enhancement Using Genre and Object-Based Edit Selection
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Solution Overview
Problem
Current image enhancement mechanisms fail to provide customization and efficiency, often resulting in cognitive overload and suboptimal aesthetic enhancements due to standardized pre-defined themes and manual creation, which do not account for the specific characteristics of individual images.
Innovation Solution
A system utilizing genre and object identification machine-learning models to automatically determine image characteristics, compare them to an image enhancement library, and apply edits based on aesthetic value measurements to recommend enhancements tailored to the image's genre and objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If standard pre-defined filters are offered for all images, then users have an avenue to quickly edit their images, but users find it difficult to select an appropriate filter and the filters do not always result in desired enhancements
Solution Approach 1:
The patent segments the image enhancement process by first analyzing image characteristics (genre, objects, lighting, composition) and then selecting from multiple candidate filters. This segmentation allows the system to move from a single standardized filter approach to a customized filter selection process that adapts to different image types while maintaining ease of use through automated analysis.
Solution Approach 2:
The patent changes the parameters of filter selection by using machine learning models to analyze image-specific parameters (genre, objects, lighting conditions, composition quality) and dynamically select filters based on these parameters. This replaces the static parameter approach of offering the same filters for all images with a dynamic parameter-based selection process.
2Adaptability or versatility
If numerous standard filters are provided for user selection, then users have more editing options, but the large number of filters creates cognitive overload and makes it difficult to select an appropriate filter
Solution Approach 1:
The patent extracts the decision-making process from the user by automatically analyzing image characteristics and selecting appropriate filters. Instead of presenting users with numerous filter options to choose from, the system extracts the selection task and performs it automatically based on image analysis, thereby reducing interface complexity while maintaining filter variety.
Solution Approach 2:
The patent implements feedback mechanisms where the system analyzes image characteristics, selects candidate filters, applies them, and then evaluates the results using aesthetic analysis. This feedback loop allows the system to automatically refine filter selection based on actual enhancement outcomes, reducing the need for complex user interaction while maintaining high adaptability.
3Manufacturing precision
If manual image enhancement is performed to achieve high-quality results, then image quality is improved, but users do not have the time or proficiency to enhance images before use
Solution Approach 1:
The patent performs preliminary actions by automatically analyzing image characteristics (genre, objects, lighting, composition) and pre-selecting appropriate filters before the user needs to make decisions. This preliminary automated analysis and filter selection process eliminates the need for users to spend time manually evaluating and selecting filters, while still achieving high-quality enhancements through careful filter choice.
Solution Approach 2:
The patent enables self-service image enhancement by allowing the system to automatically perform the enhancement process without requiring user proficiency in image editing techniques. The machine learning models and aesthetic analysis algorithms serve themselves to select and apply appropriate filters, freeing users from needing time or expertise in manual image enhancement while maintaining high quality results.
Data Source
AI summary
A system and method and for automatically enhancing an input image includes detecting a genre for the input image using a genre identification machine-learning model and identifying one or more objects in the input image using an object identification machine-learning model. The identified genre and objects are then compared to a list of genre and object tags for images in an image library to identify a plurality of genre and object tags that are similar to the identified genre and objects. A list of edits corresponding to each of the identified similar genre and object tags is then to the input image to generate a plurality of enhanced images for the input image. An aesthetic value is measured for the plurality of enhanced images and at least one of the plurality of enhanced images is provided as a recommendation for enhancing the input image, based on the aesthetic value.