Edge-Aware Spatio-Temporal Filtering for Real-Time Video
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Solution Overview
Problem
Traditional spatio-temporal edge-aware filters face high processing latency and memory bandwidth issues due to the need for storing entire video volumes or sliding windows, and require accurate optical flow information that is difficult to obtain efficiently.
Innovation Solution
The approach involves estimating sparse optical flow using a binary feature descriptor integrated with the Coarse-to-fine PatchMatch method, followed by spatial edge-aware filtering with a permeability filter that requires only two scan-line passes per iteration, and temporal edge-aware filtering using a nonlinear infinite impulse response filter, which updates filter states incrementally based on new video frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spatio-temporal edge-aware filters operate iteratively on complete video volumes or sliding windows, then filtering accuracy is improved, but processing latency and memory bandwidth requirements increase significantly
Solution Approach 1:
The patent segments the video processing into independent frame-by-frame operations rather than processing complete video volumes or sliding windows. Each frame is filtered independently using edge-aware filters, eliminating the need to store and process large video volumes while maintaining filtering accuracy through per-frame edge detection and adaptation.
Solution Approach 2:
The patent extracts and processes only the necessary frame data in real-time without requiring the entire video volume or sliding window to be stored in memory. By taking out the essential filtering operation and applying it incrementally to each frame, the system achieves accurate edge-aware filtering while dramatically reducing memory bandwidth requirements and processing latency.
2Measurement precision
If traditional spatio-temporal edge-aware filters use complete video volumes or sliding windows, then filtering quality is improved, but memory bandwidth requirements increase
Solution Approach 1:
The patent segments video processing into independent frame operations, processing one frame at a time rather than loading complete video volumes or sliding windows into memory. This segmentation maintains filtering quality through per-frame edge detection while reducing memory bandwidth requirements by eliminating the need to transfer large video volumes between memory and processor.
Solution Approach 2:
The patent extracts only the essential frame data needed for filtering and processes it in real-time without requiring large memory allocations for video volumes. By taking out the core filtering operation and applying it incrementally, the system achieves high-quality edge-aware filtering with minimal memory bandwidth consumption.
3Measurement precision
If accurate optical flow information is used for temporal filtering alignment, then filtering accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent uses approximate optical flow estimation methods that are computationally inexpensive rather than accurate but complex methods. By using simpler, faster optical flow algorithms that provide sufficient alignment for edge-aware filtering, the system achieves good filtering accuracy while dramatically reducing computational complexity and processing time, making real-time processing feasible.
Data Source
AI summary
The disclosure provides an approach for edge-aware spatio-temporal filtering. In one embodiment, a filtering application receives as input a guiding video sequence and video sequence(s) from additional channel(s). The filtering application estimates a sparse optical flow from the guiding video sequence using a novel binary feature descriptor integrated into the Coarse-to-fine PatchMatch method to compute a quasi-dense nearest neighbor field. The filtering application then performs spatial edge-aware filtering of the sparse optical flow (to obtain a dense flow) and the additional channel(s), using an efficient evaluation of the permeability filter with only two scan-line passes per iteration. Further, the filtering application performs temporal filtering of the optical flow using an infinite impulse response filter that only requires one filter state updated based on new guiding video sequence video frames. The resulting optical flow may then be used in temporal edge-aware filtering of the additional channel(s) using the nonlinear infinite impulse response filter.