Region-Based Video Warping for Temporal Coherence
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Solution Overview
Problem
Existing video resizing methods face challenges in maintaining temporal coherence and avoiding geometric distortion, especially when adapting videos for display at different sizes and aspect ratios, often requiring significant computational resources and failing to handle camera and object motion effectively.
Innovation Solution
The method generates a motion history map to identify salient regions and weight pixels differently for background and salient regions, warping video frames to preserve shape and temporal coherence while minimizing distortion, using a limited number of adjacent frames to reduce computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional video resizing methods are used to adapt video for different display sizes, then the video can be displayed on various devices, but temporal coherence is lost resulting in jitter and geometric distortion occurs
Solution Approach 1:
The patent applies preliminary action by using a limited number of adjacent frames to predict motion and establish warping transformations before the actual resizing operation. This predictive approach pre-establishes the geometric relationships between frames, enabling consistent warping across the video sequence and maintaining temporal coherence while adapting to different display sizes.
Solution Approach 2:
The patent implements dynamics by making the warping transformation adaptive and frame-dependent rather than static. The system dynamically adjusts the warping parameters for each frame based on motion analysis and saliency detection, allowing the transformation to evolve with the video content while preserving temporal consistency across frames.
2Reliability
If intensive spatio-temporal optimization is applied to maintain temporal coherence, then jitter is reduced, but computational resources increase significantly
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different regions within video frames. Saliency detection identifies important regions that require higher fidelity warping, while less important regions use simplified transformations. This localized approach concentrates computational resources where they are most needed, maintaining temporal coherence in critical areas without uniformly processing the entire frame.
Solution Approach 2:
The patent implements partial action by using a limited subset of adjacent frames for motion prediction rather than analyzing the entire video sequence. This selective approach applies warping optimization only to the necessary temporal window, achieving sufficient temporal coherence with reduced computational overhead compared to exhaustive spatio-temporal optimization.
3Shape
If seam carving or spatially varying warp methods are used for image resizing, then salient regions are preserved, but the methods are challenging to extend to video and require significant computational resources
Solution Approach 1:
The patent applies segmentation by dividing the video processing into distinct stages: saliency detection, motion estimation, and warping transformation. Each stage processes specific aspects of the video content independently, allowing salient regions to be identified and preserved while simplifying the overall computational complexity through modular processing.
Data Source
AI summary
Video data is processed with region-based warping. In accordance with various example embodiments, video data having a plurality of source video frames is processed as follows, for each source video frame. Pixels representing the source video frame and at least one temporally-adjacent video frame are used to generate a motion history map, based upon frame-to-frame movement of at least one region in the source video frame and temporally-adjacent video frames. At least one salient region is identified in the source video frame based upon the motion history map and static image saliency data. Weighting data is determined for warping pixels corresponding to background regions of the source video frame differently than pixels corresponding to the at least one salient region in the source video frame. Using the weighting data, the pixels representing the source video frame are warped to generate pixels representing a warped video frame.