3D Image Format Identification via Feature Correspondence
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Solution Overview
Problem
Existing technologies fail to effectively distinguish between three-dimensional (3D) and two-dimensional (2D) image formats and identify the specific 3D format of an image, leading to processing and display challenges for image viewers and video players.
Innovation Solution
A method using feature correspondence determination to identify 3D image formats by sampling received images into sub-images, comparing their similarity, detecting features, forming correspondences, and analyzing positional differences to determine if an image is 2D or 3D and which specific 3D format it represents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple 3D image frame formats are supported, then the adaptability of the system is improved, but the device complexity increases due to the need to handle various formats (side-by-side, checkerboard, interlaced, top-bottom, color-based)
Solution Approach 1:
The patent applies preliminary action by performing format identification and classification before actual image processing. The system analyzes the image frame stream in advance to determine whether it contains 3D content and what specific format is used, allowing the receiver to prepare appropriate processing parameters beforehand. This prevents the need for complex real-time format detection during playback.
Solution Approach 2:
The patent introduces an intermediary component that acts as a bridge between the received image stream and the display system. This intermediary performs feature analysis, generates format identification information, and communicates it to the receiver, thereby simplifying the overall system architecture by centralizing the complex format detection functionality in a dedicated module.
2Measurement precision
If feature correspondence determination is used to identify 3D formats, then the measurement precision of format identification is improved, but the loss of time increases due to sampling, feature detection, and correspondence analysis
Solution Approach 1:
The patent applies partial action by performing feature detection and correspondence analysis on only a sampled portion of the image frame rather than the entire image. By selecting representative features and comparing them across frames, the system achieves sufficient accuracy for format identification without processing every pixel, thereby reducing the time overhead significantly.
3Productivity
If 3D image frames are combined into a single frame for storage and distribution, then the productivity of image transmission is improved, but the difficulty of detecting and measuring increases because the single frame may appear similar to 2D image frames without further analysis
Solution Approach 1:
The patent utilizes color changes and color channel analysis as indicators for 3D format detection. Different 3D formats (such as anaglyph, side-by-side with color coding, or interlaced with color alternation) exhibit specific color patterns and channel relationships that distinguish them from standard 2D images. By analyzing color distribution, channel correlation, and spectral characteristics, the system can automatically identify 3D content within the combined frame structure.
Data Source
AI summary
A method identifies the presence of a three-dimensional (3D) image format in received image through the use of feature matching and correspondence. The received image is sampled using a candidate 3D format to generate two sub-images from the received image. Initially, these sub-images are compared to determine whether these sub-images are similar with respect to structure. If the sub-images are not similar, a new 3D format is selected and the method is repeated. If the sub-images are similar, features are detected in the two sub-images and a correspondence is formed between features that match in the two sub-images. Positional differences are computed between corresponding features. The amount and uniformity of the positional differences are then used to determine whether the format is 2D or 3D and, if 3D, which of the 3D formats was used for the received image.