Video Frame Type Detection Using Color Histograms
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Solution Overview
Problem
Existing methods for determining video frame type, such as stereoscopic video displays, require supplementary data or signals, which can be troublesome for inexperienced users and may not be recognizable by all video display units, especially when the video signal lacks indication of frame type.
Innovation Solution
A computer-implemented method that analyzes video frames by extracting portions from distinct quarters, calculating color histograms, comparing them, and generating a frame type indicator based on the comparison results, allowing for the determination of 3D TB-type or 3D LR-type frames without supplementary data, and optionally compacts the frame by discarding or scaling down non-active regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supplementary data or signals are used to indicate video frame type, then the accuracy of frame type determination is improved, but the device complexity and user operation difficulty increase
Solution Approach 1:
The video signal itself contains all necessary information for frame type determination through its intrinsic structure. The method analyzes the arrangement and correlation of image blocks within the video frame to automatically identify whether it is an interlaced or progressive scan signal, without requiring any external supplementary data or user intervention. This self-service approach eliminates the need for additional decoding complexity while maintaining determination accuracy.
Solution Approach 2:
The invention extracts frame type determination capability directly from the video signal structure itself, separating this function from external supplementary data requirements. By analyzing the spatial arrangement and temporal correlation of image blocks within the frame, the method extracts sufficient information to determine frame type without relying on external metadata or control signals.
2Measurement precision
If supplementary data or signals are used to indicate video frame type, then the frame type determination accuracy is improved, but the ease of operation for inexperienced users deteriorates
Solution Approach 1:
The system performs automatic frame type determination through self-service analysis of the video signal structure, requiring no user input or manual configuration. The method automatically detects interlaced or progressive scan formats by analyzing image block arrangements, making the device equally easy to operate for both inexperienced and expert users while maintaining high determination accuracy.
Solution Approach 2:
Instead of requiring users to manually specify or configure frame type information, the invention inverts the approach by having the system automatically derive frame type characteristics from the video signal structure itself. This inversion transforms a potentially complex user task into an automatic background process.
3Ease of operation
If the video signal contains only basic image contents without frame type indication, then the ease of operation is improved, but the frame type determination capability deteriorates
Solution Approach 1:
The invention changes the analysis parameters from relying on explicit frame type indicators to analyzing fundamental signal characteristics such as the spatial arrangement and temporal correlation of image blocks. By shifting to these alternative parameters that are inherently present in all video signals regardless of format, the method maintains determination capability while working with simple basic image contents.
Solution Approach 2:
The analysis method is designed to be universal and applicable to all video signal types (interlaced, progressive, different resolutions) without requiring format-specific indicators. By analyzing common structural elements present in all video frames, the method achieves multi-functional determination capability that works with any basic video signal content.
Data Source
AI summary
A computer-implemented method for determining whether a video frame is of a 3D TB type (Top-Bottom) or a 3D LR type (Left-Right) frame, characterized in that it comprises the steps of: receiving a video frame (100); extracting at least three portions (121-124) of the frame, each portion belonging to a distinct quarter of the frame (100, 120) and being positioned at the same fragment of the quarter; calculating color histograms for each portion (121-124); comparing the color histograms of at least two different pairs of portions; generating a frame type indicator based on the result of comparison of the color histograms.