Content-Aware Video Retargeting via Non-Uniform Pixel Warp
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Solution Overview
Problem
Conventional video retargeting techniques fail to provide a comfortable viewing experience due to unnatural object proportions and visible discontinuities, especially when scaling video content for devices with different display formats, as they lack uniform scaling and temporal coherence.
Innovation Solution
An integrated system for content-aware video retargeting that combines key frame-based constraint editing with automatic algorithms, using a non-uniform, pixel-accurate warp considering video saliency, edge preservation, and scene cut detection, along with elliptical weighted average splatting to reduce aliasing artifacts, allowing for real-time retargeting to arbitrary aspect ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional linear downscaling is used for video retargeting, then the video can be scaled to fit target platform formats, but the object proportions appear unnatural and viewing experience deteriorates
Solution Approach 1:
The patent applies non-uniform scaling differentially across the video frame, preserving uniform scaling for important content regions while allowing non-uniform scaling for background or less important areas. This is achieved through content-aware retargeting that identifies salient objects and maintains their proper aspect ratios while still adapting the overall video to target formats.
Solution Approach 2:
The video is segmented into important content regions and background regions based on saliency detection. Different scaling transformations are applied to each segment independently, allowing important objects to maintain their natural proportions while the overall composition is adapted to the target platform.
2Adaptability or versatility
If cropping or panning techniques are used to remove unimportant content, then the video can be adapted to target formats, but temporal coherence and scene composition are compromised
Solution Approach 1:
The patent uses dynamic warping that adapts the video transformation based on detected scene content and temporal relationships. The warping parameters are adjusted frame-by-frame or scene-by-scene to maintain temporal coherence, allowing the video to be dynamically retargeted while preserving scene composition stability across time.
Solution Approach 2:
The system incorporates feedback from saliency detection and scene analysis to continuously adjust the retargeting transformation. By monitoring which regions are identified as important and how scenes change over time, the system refines the warping to maintain both format adaptability and temporal coherence.
3Adaptability or versatility
If manifold seam techniques are used to remove unimportant pixels, then some content can be removed, but large scale changes result in seams cutting through feature regions and visible discontinuities occur
Solution Approach 1:
The patent introduces an intermediary warping transformation that smoothly bridges the source video and target format requirements. Instead of directly removing pixels through seams, the warping acts as an intermediary that gradually transforms the image content, avoiding abrupt discontinuities and preserving pixel-level continuity while still achieving format adaptation.
Solution Approach 2:
The system uses parameter-based warping transformations rather than pixel-based seam removal. By changing the transformation parameters (scale, position, distortion) in a continuous and controlled manner, the system avoids the discontinuities and seams that result from abrupt pixel removal, while still achieving the desired content adaptation.
4Productivity
If conventional video retargeting approaches are used, then video can be rescaled, but uniform scaling of important image content cannot be achieved
Solution Approach 1:
The video is segmented into salient and non-salient regions based on content analysis. The retargeting process then applies uniform scaling to salient regions while allowing flexible transformation for non-salient regions. This segmentation enables the system to maintain fast processing by focusing computational resources on preserving important content while still achieving overall format adaptation.
Data Source
AI summary
Techniques are provided for content-aware video retargeting. An interactive framework combines key frame-based constraint editing with numerous automatic algorithms for video analysis. This combination gives content producers a high level of control of the retargeting process. One component of the framework is a non-uniform, pixel-accurate warp to the target resolution that considers automatic as well as interactively-defined features. Automatic features comprise video saliency, edge preservation at the pixel resolution, and scene cut detection to enforce bilateral temporal coherence. Additional high level constraints can be added by the producer to achieve a consistent scene composition across arbitrary output formats. Advantageously, embodiments of the invention provide a better visual result for retargeted video when compared to using conventional techniques.