3D Blue Noise Mask for Video Sampling
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Solution Overview
Problem
Existing video sampling methods, particularly those using blue noise masks, face challenges in maintaining temporal consistency and spatial quality, leading to potential artifacts and inefficiencies in video compression and processing, especially in artificial reality systems with limited resources.
Innovation Solution
Generating a Manhattan blue noise mask that considers the temporal domain, using Manhattan distance for temporal consistency and Euclidean distance for spatial prioritization, and employing motion-compensated spatiotemporal sampling to reduce the number of samples needed, by calculating optical flow between frames to transform sampling locations and focus on areas with lacking information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a blue noise mask is used to sample a video, then spatial quality is improved, but temporal consistency deteriorates
Solution Approach 1:
The patent transitions from traditional 2D blue noise masks to 3D spatiotemporal blue noise masks that incorporate the temporal dimension. This allows the sampling pattern to maintain blue noise properties in both spatial and temporal domains, resolving the contradiction between spatial quality and temporal consistency by adding a temporal dimension to the sampling mask structure.
Solution Approach 2:
The patent employs motion-compensated sampling that dynamically adapts the sampling pattern based on optical flow between frames. The sampling mask is adjusted according to motion vectors, allowing it to maintain temporal consistency while preserving spatial quality by following the dynamic content of the video.
2Productivity
If motion compensation is applied to reduce samples needed, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent changes the parameters of the sampling mask by incorporating motion vectors and optical flow information. The sampling density and location are dynamically adjusted based on motion parameters, allowing efficient skipping of redundant samples in static regions while maintaining high sampling accuracy in regions with motion or detail changes.
3Manufacturing precision
If Euclidean distance is used for spatial prioritization, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the video processing into distinct stages: optical flow calculation, motion-compensated sampling location determination, and blue noise mask application. This segmentation allows each component to be optimized independently, reducing overall computational complexity while maintaining spatial distribution accuracy through the use of Euclidean distance in the blue noise mask generation.
Data Source
AI summary
In one embodiment, a computing system may receive a video including a sequence of frames. The computing system may access a three-dimensional mask that specifies pixel-sampling locations, the three-dimensional mask having a first dimension and a second dimension corresponding to a spatial domain and a third dimension corresponding to a temporal domain. Blue noise property may be present in the pixel-sampling locations that are associated with each of a plurality of two-dimensional spatial slices of the three-dimensional mask in the spatial domain and the pixel-sampling locations that are associated with each of a plurality of one-dimensional temporal slices of the three-dimensional mask in the temporal domain. The computing system may generate a sample of the video by sampling the sequence of frames using the three-dimensional mask.