Frame Interpolation Using Spatial Weighting for Video Artifacts
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Solution Overview
Problem
Existing frame interpolation techniques fail to account for spatial considerations in non-uniform image capture, leading to artifacts such as ghostly or blurry images, as they treat all pixels uniformly without regard to lens distortion and optical flow, resulting in degraded image quality.
Innovation Solution
The method involves generating interpolated frames by assigning differing weights to pixel information based on capture parameters and optical flow, considering spatial and temporal factors to accurately reflect the quality of visual content, thereby improving the interpolation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform interpolation techniques are used for all pixels, then the processing complexity is reduced, but the image quality deteriorates due to artifacts in non-uniform image capture
Solution Approach 1:
The patent applies local quality by assigning different weights to different spatial regions and pixel types within the image. Specifically, it distinguishes between distorted and undistorted regions based on capture parameters, and applies different interpolation weighting strategies to each region. This allows the system to maintain simplicity in undistorted regions while applying more complex artifact-reduction techniques only where needed, thus resolving the contradiction between processing complexity and image quality.
Solution Approach 2:
The patent segments the image into different regions based on distortion characteristics. It identifies distorted regions (where lens distortion artifacts are present) and undistorted regions separately, and applies different interpolation weightings to each segment. This segmentation approach allows the system to handle complex artifact correction only in necessary regions, reducing overall processing complexity while improving image quality in problematic areas.
2Manufacturing precision
If spatial considerations are incorporated into frame interpolation, then image quality improves, but processing burden increases
Solution Approach 1:
The patent reduces processing burden by applying spatial considerations selectively rather than uniformly. It uses capture parameters to identify only those regions where spatial weighting is necessary (distorted regions), and applies simplified uniform weighting in undistorted regions. This localized application of complex algorithms minimizes the overall processing burden while still achieving improved image quality where needed.
Solution Approach 2:
The patent performs preliminary analysis of capture parameters before the actual frame interpolation process. By pre-identifying distorted regions and determining appropriate weighting strategies in advance, the system avoids performing complex spatial analyses during the time-critical interpolation step itself. This preliminary action reduces the real-time processing burden while maintaining image quality improvements.
3Ease of operation
If straightforward averaging of pixel values is used, then processing is simplified, but artifacts such as ghostly images appear in the output
Solution Approach 1:
The patent maintains processing simplicity by using straightforward averaging in undistorted regions while applying weighted averaging only in distorted regions. The system uses capture parameters to identify distorted regions where artifacts are likely to occur, and applies different weighting strategies specifically to those regions. This selective approach eliminates ghostly artifacts where they matter most while keeping the overall processing simple and efficient.
4Quantity of substance
If uniform weighting is applied to all pixel information, then memory consumption is reduced, but interpolation accuracy deteriorates in non-uniform capture scenarios
Solution Approach 1:
The patent optimizes memory consumption by applying differential weighting only to specific pixel regions where it is necessary. It uses capture parameters to identify distorted regions and applies additional weighting information only to those regions, while using uniform weighting in undistorted regions. This localized approach to memory-intensive operations reduces overall memory consumption while maintaining interpolation accuracy in the critical distorted regions.
Data Source
AI summary
Apparatus and methods for the generation of interpolated frames of video data. In one embodiment, a computerized apparatus is disclosed that includes a video data interface configured to receive frames of video data; a processing apparatus in data communication with the video data interface; and a storage apparatus in data communication with the processing apparatus. The computerized apparatus is further configured to: receive frames of captured video data; retrieve capture parameters associated with the frames of captured video data; generate optical flow parameters from the frames of captured video data; ascribe differing weights based on the capture parameters and/or the optical flow parameters; generate frames of interpolated video data for the frames of captured video data based at least in part on the ascribed weights; and compile a resultant video stream using the frames of interpolated video data and the frames of captured video data.