Episodic Image Selection for Video Content
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Solution Overview
Problem
The significant amount of video content available through video on demand services makes it difficult and time-consuming to manually identify representative images for each item, which can lead to user confusion when browsing content.
Innovation Solution
A computing environment that attributes image quality scores, identifies episodic image candidates, scales scores based on selection factors, and filters images to select representative episodic images for display in user interfaces, using an episodic image engine that processes video content to select and filter images based on quality, text presence, face detection, and time codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of representative images is performed, then image selection accuracy can be maintained, but time consumption and labor effort increase significantly
Solution Approach 1:
The system enables automatic self-selection of representative images through machine learning models that autonomously analyze video content, extract frames, evaluate quality metrics, and select final images without human intervention, replacing manual curation while maintaining selection accuracy
Solution Approach 2:
The patent replaces the mechanical manual process of image selection with an automated computational system that uses video analysis, quality scoring algorithms, and machine learning models to perform the same function more efficiently and at scale
2Adaptability or versatility
If the number of video content items increases, then service coverage and content variety improve, but user confusion and difficulty in identifying representative images increase
Solution Approach 1:
The system applies different quality evaluation criteria and selection factors to different types of video content, adjusting the representation strategy based on content characteristics such as genre, duration, and target audience to optimize user experience for each content category
Solution Approach 2:
The patent uses visual enhancements including color adjustments, contrast modifications, and image filtering to make selected representative images more visually distinctive and appealing, helping users quickly identify and differentiate between various video content items
3Measurement precision
If comprehensive image quality evaluation is performed, then selection quality improves, but processing complexity and computational resources increase
Solution Approach 1:
The image selection process is divided into distinct modular stages including video parsing, frame extraction, quality evaluation, and final selection, with each stage handling specific tasks independently to reduce overall processing complexity while maintaining comprehensive evaluation
Solution Approach 2:
The system implements multi-factor evaluation including quality metrics, text presence, face detection, and time code analysis, applying more evaluation criteria than strictly necessary to ensure high selection quality, with the ability to adjust the level of evaluation based on resource availability
Data Source
AI summary
The targeted selection of certain representative images from content is described. In one example case, images in video content are first attributed a quality score based on an image quality factor, such as the brightness and contrast of the images. A subset of the images are identified as episodic image candidates using an image quality threshold. Image scores of one or more of the episodic image candidates are scaled based on one or more episodic image selection factors. Episodic images for the video content are selected from among the episodic image candidates using one or more episodic image selection rules. In some cases, a number of the episodic images are filtered out, for example, as being too similar to each other or for failing to be distinguishable from episodic images of other content. The episodic images are provided for use as representative images for the video content.