Image Quality Scoring for Network Publications
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Solution Overview
Problem
Network-based publication systems face challenges in effectively evaluating and ranking images for display, leading to suboptimal sales and revenue, as existing methods lack a systematic approach to correlate image features with user engagement data.
Innovation Solution
A system and method that involves a learning phase to correlate image features (such as size, contrast, and color) with user interaction data (like clicks and sales) to determine image quality scores, which are then used to prioritize image display on search results pages, combining computer vision analysis and human input for accurate scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If images are displayed without systematic quality scoring, then the system operates simply, but image ranking effectiveness deteriorates leading to suboptimal sales
Solution Approach 1:
The system collects feedback data from user interactions (clicks, views, sales) and uses this feedback to continuously refine and update image quality scores. This feedback loop enables the system to learn what constitutes high-quality images for different contexts and improve ranking effectiveness over time without requiring complex manual evaluation processes.
Solution Approach 2:
The patent replaces manual mechanical image evaluation processes with automated computer vision algorithms and machine learning models. These systems automatically extract features from images and predict their quality based on user interaction patterns, eliminating the need for human reviewers while maintaining high accuracy in image ranking.
2Measurement precision
If manual image evaluation methods are used, then scoring accuracy can be maintained, but processing time and operational cost increase
Solution Approach 1:
The system performs self-evaluation of image quality using automated algorithms that continuously learn from user interaction data. Rather than requiring external human evaluation, the system autonomously extracts image features, predicts quality metrics, and refines its scoring based on actual user behavior patterns, achieving both speed and accuracy.
Solution Approach 2:
The system dynamically adjusts evaluation parameters and weighting factors based on changing user behavior patterns and market conditions. By adapting the scoring criteria to reflect current user preferences and interaction patterns, the system maintains high accuracy while processing images rapidly through automated feature extraction and machine learning models.
3Reliability
If image features are not correlated with user interaction data, then the system remains simple, but the ability to predict sales performance deteriorates
Solution Approach 1:
The system builds a universal image quality scoring model that serves multiple functions: it evaluates image aesthetics, predicts user engagement, estimates sales performance, and optimizes ranking. By integrating multiple data sources (image features, user interactions, contextual information) into a single comprehensive scoring framework, the system achieves high reliability across different prediction tasks without requiring separate specialized systems.
Solution Approach 2:
The patent transforms the evaluation from a single-dimensional aesthetic judgment to a multi-dimensional analysis that incorporates image features, user interaction patterns, contextual metadata, and temporal factors. This dimensional expansion enables the system to capture complex relationships between images and user behavior, improving prediction accuracy while managing complexity through structured data organization and machine learning techniques.
Data Source
AI summary
A system receives images of objects. The system identifies a category for each of the objects, and extracts features from the images. The features relate to a quality of the image. The features of the images are stored in a database according to the category of each object, such that each set of features is associated with its corresponding image. The system displays the images on a network-based publication system, and receives data relating to the displayed images. The data is analyzed, and the images are ranked as a function of the analysis. The system redisplays the images on the network-based publication system as a function of the ranking of the images.