Visual Cue Suitability Detector for Monoscopic to Stereoscopic 3D Conversion
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Solution Overview
Problem
Conventional methods for converting monoscopic visual content to stereoscopic 3D are inefficient and rely on unreliable metadata or time-consuming manual processes, making it difficult to determine suitability and quality for conversion.
Innovation Solution
A suitability detector analyzes visual cues such as histogram, edge, and separator cues from video frames to automatically determine if the content is suitable for conversion to stereoscopic 3D, considering factors like video quality and length, and an image converter can then convert the content if suitable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional conversion techniques are used to convert 2D images and video to 3D, then conversion capability is provided, but efficiency is poor when converting large numbers of images or video
Solution Approach 1:
The system automatically determines suitability for conversion by analyzing visual content characteristics such as depth cues, motion patterns, and scene complexity, eliminating the need for manual inspection and enabling high-volume automated conversion processing
Solution Approach 2:
The system performs preliminary analysis of visual content to identify suitability for conversion before the actual conversion process, using pre-defined criteria such as presence of depth cues, motion characteristics, and scene type to filter and prioritize content for automated conversion
2Reliability
If metadata examination is used to determine suitability for conversion, then a quick assessment is provided, but reliability is notoriously unreliable
Solution Approach 1:
The system replaces unreliable metadata examination with automated visual analysis that directly processes image and video content, using computer vision algorithms to detect depth cues, motion patterns, and scene characteristics for accurate suitability determination
Solution Approach 2:
The system introduces an intermediary automated analysis layer between the raw visual content and conversion decision, using intermediate metrics such as depth cue density, motion complexity scores, and scene type classification to bridge content analysis and suitability determination
Data Source
AI summary
A suitability detector identifies a plurality of frames of an input video. The suitability detector determines, based on characteristics of the plurality of frames, whether the input video is suitable for conversion from monoscopic visual content to stereoscopic 3D. The characteristics may include a visual cue present in the plurality of frames and a visual quality of the plurality of frames. If the input video is suitable for conversion, an image converter converts the input video to stereoscopic 3D.