Motion Vector Field Segmentation for Video Frame Interpolation
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Solution Overview
Problem
Existing technologies face challenges in efficiently converting video with 3D graphics from one framerate and/or resolution to another, while minimizing computational power and reducing the instability of inpainting algorithms when dealing with large holes.
Innovation Solution
A method involving the calculation, processing, and filtering of motion vector (MV) fields to generate internal and external MV fields, which are then used to create depth fields for frame interpolation and hole reduction, allowing for more accurate and stable image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional motion vector calculation methods are used for frame interpolation, then framerate conversion can be achieved, but computational power requirements become excessive
Solution Approach 1:
The motion vector field is segmented into multiple depth layers (foreground, midground, background) based on depth information. Each layer is processed independently with tailored motion compensation strategies, allowing the system to focus computational resources only where necessary rather than processing the entire frame uniformly, thus reducing overall computational power consumption while maintaining framerate conversion capability.
Solution Approach 2:
Different quality levels and processing strategies are applied to different regions of the frame based on depth and motion characteristics. High-motion regions and foreground objects receive more sophisticated motion compensation, while static or low-priority regions use simpler methods. This localized quality approach maintains visual fidelity where needed while reducing computational burden in less critical areas.
2Productivity
If simple inpainting algorithms are used to fill holes in frame interpolation, then processing speed is maintained, but output stability deteriorates
Solution Approach 1:
Depth information and motion vectors are calculated and prepared in advance for all regions before inpainting is performed. The system pre-identifies hole regions and prepares appropriate fill strategies based on surrounding depth layers and motion patterns. This preliminary preparation ensures that when inpainting is executed, stable and consistent results are achieved without requiring multiple iterative passes, thus maintaining processing speed while improving output stability.
Solution Approach 2:
Depth maps serve as an intermediary layer that guides the inpainting process. Rather than directly filling holes in the image space, the system uses depth information to guide motion-compensated reconstruction from reference frames. This intermediary depth guidance ensures that inpainted regions are consistent with the 3D scene structure, improving stability without sacrificing processing efficiency.
Data Source
AI summary
Systems and method are herein provided for motion vector calculation, processing, and filtering. In one example, a method comprises receiving a plurality of image frames as inputs; calculating one or more internal motion vector (MV) fields between a past frame (PF) and a current frame (CF) of the plurality of image frames; generating a foreground MV field and a background MV field of the one or more MV fields; processing the one or more internal MV fields and the foreground and background MV fields to generate one or more depth fields, wherein processing the one or more internal MV fields comprises generating virtual depths of the one or more internal MV fields to generate one or more MVD fields; and outputting the one or more MVD fields for image processing.