Snap inc. (20240296535). AUTOMATIC IMAGE QUALITY EVALUATION simplified abstract
Contents
AUTOMATIC IMAGE QUALITY EVALUATION
Organization Name
Inventor(s)
Mykyta Bakunov of Adliswil (CH)
Pavel Savcenkov of London (GB)
Sergey Smetanin of London (GB)
Jian Ren of Marina Del Ray CA (US)
AUTOMATIC IMAGE QUALITY EVALUATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240296535 titled 'AUTOMATIC IMAGE QUALITY EVALUATION
The patent application describes techniques for automatic image quality evaluation using machine learning models.
- Images generated by automated image generators are accessed and quality indicators are generated for each image.
- Images are automatically selected and compared based on the quality indicators.
- A ranking of the image generators is generated based on the comparison results.
- The ranking data is presented on a device for user review.
Potential Applications: - Automated image quality assessment in various industries such as photography, advertising, and e-commerce. - Enhancing image selection processes in content creation platforms and social media.
Problems Solved: - Eliminates the need for manual image quality evaluation, saving time and resources. - Ensures consistent and objective assessment of image quality.
Benefits: - Improved efficiency in image selection processes. - Enhanced user experience through better quality images. - Increased productivity in content creation workflows.
Commercial Applications: Title: Automated Image Quality Evaluation Technology for Content Creation Platforms This technology can be utilized in content creation platforms to streamline image selection processes, improve content quality, and enhance user engagement. It can also be integrated into e-commerce platforms to ensure high-quality product images, leading to increased sales and customer satisfaction.
Questions about Automated Image Quality Evaluation Technology: 1. How does this technology impact the efficiency of image selection processes? - This technology improves efficiency by automating the image quality evaluation process, saving time and resources for users. 2. What are the potential benefits of using machine learning models for image quality assessment? - Machine learning models provide consistent and objective image quality evaluation, leading to improved overall quality of selected images.
Original Abstract Submitted
examples disclosed herein describe techniques for automatic image quality evaluation. a first set of images generated by a first automated image generator and a second set of images generated by a second automated image generator are accessed. a first machine learning model generates a first quality indicator for each image in the first set of images and the second set of images. a second machine learning model generates a second quality indicator for each image in the first set of images and the second set of images. based on the generated indicators, a first image from the first set of images and a second image from the second set of images are automatically selected and compared. a first ranking of the first automated image generator and the second automated image generator is generated based on the comparison, and ranking data is caused to be presented on a device.
