Frequency Domain Frame Interpolation for High-Resolution Video
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Solution Overview
Problem
Existing frame interpolation methods, particularly those relying on optical flow, become inefficient for high-resolution, high-frame-rate videos due to their computational intensity and dependency on global optimization, which is not well-suited for large-scale, densely sampled video data.
Innovation Solution
The system employs frequency domain representations to interpolate frames by decomposing spatial domain representations into sinusoidal functions, identifying corresponding functions, determining phase differences, and interpolating these functions to generate new frames, thereby reducing computational cost while maintaining or improving interpolation quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optical flow-based global optimization methods are used for frame interpolation, then pixel correspondence accuracy is improved, but computational cost and processing time increase significantly for high-resolution, high-frame-rate videos
Solution Approach 1:
The patent divides the video processing task into multiple levels of a pyramid structure, where the video is progressively down-sampled into smaller representations. Frame interpolation is performed at each level, allowing the system to work on smaller data sets that require less computational resources while maintaining accuracy through the hierarchical approach.
Solution Approach 2:
The patent transforms the problem from spatial domain to frequency domain by representing video frames as sums of sinusoidal functions with different frequencies, amplitudes, and phases. This dimensional transformation allows motion to be represented as phase shifts, enabling efficient interpolation through simple phase calculations rather than complex spatial optimization.
2Manufacturing precision
If standard optical flow techniques are applied to high-resolution, high-frame-rate video, then frame interpolation quality may be maintained, but the computational complexity becomes inefficient for large-scale, densely sampled video data
Solution Approach 1:
The patent applies multi-resolution pyramid segmentation where the video is processed at multiple scales from coarse to fine. This allows the system to capture both global motion patterns at lower resolutions and local details at higher resolutions, maintaining interpolation quality while reducing overall computational complexity compared to processing the full-resolution video directly.
Solution Approach 2:
The patent changes the representation parameters from spatial pixel values to frequency domain parameters (amplitude, frequency, phase). By representing frames as sinusoidal functions and motion as phase shifts, the system achieves accurate interpolation with simpler operations that reduce computational complexity while preserving quality.
3Productivity
If frequency domain representation with phase shift method is used, then computational cost is reduced, but handling of large displacements may become challenging
Solution Approach 1:
The patent uses a multi-level pyramid structure where interpolation is performed progressively from coarse to fine resolutions. For large displacements, the coarse levels capture the majority of the motion as phase shifts, while finer levels refine the result. This segmentation approach maintains reliability for large displacements by breaking them into manageable increments across multiple scales.
Solution Approach 2:
The patent performs preliminary interpolation at coarser resolution levels before refining at finer levels. This preliminary action captures large-scale motion patterns early in the process, allowing the subsequent fine-level processing to focus on smaller residual displacements, thereby maintaining accuracy for large overall displacements while preserving computational efficiency.
Data Source
AI summary
Interpolating frames of a video may provide a technique for one or more of frame rate conversion, temporal upsampling for generating slow motion video, image morphing, virtual view synthesis, and/or other video applications. A system may be configured to interpolated frames of a video by leveraging frequency domain representations of individual frames. The frequency domain representations may be decomposed into set of discrete functions that make up the frequency domain representations. Corresponding functions from sets of functions associated with frames with which an interpolated frame is to be determined may be identified. Phase differences between corresponding functions may be determined. Interpolated functions between the corresponding functions may be determined based on the determined phased differences. Information describing spatial domain representations of interpolated frames may be determined based on the interpolated functions.