Reversible Image Stylization Using Compressed Transformation Metadata
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern image and video processing technologies face challenges in achieving efficient and reversible stylization without storing both the original and styled assets, leading to computational inefficiencies and storage overheads, particularly due to the instability of semantic mask-based approaches and the complexity of video segmentation networks.
Innovation Solution
A system that learns a compressed, latent mathematical representation of the transformation from an unstyled to a styled asset, allowing reversible stylization by storing only the styled output and metadata for reverse transformation, reducing storage needs and enabling efficient restyling without duplicating full-resolution assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic mask-based approaches are used for image stylization, then processing precision is improved, but computational stability deteriorates due to mask boundary instability and flicker
Solution Approach 1:
The patent extracts and removes the semantic mask application step from the stylization pipeline. Instead of using semantic masks to guide stylization, the system directly applies style transfer algorithms to the original image, eliminating the source of mask instability and flicker while maintaining processing precision through alternative methods
Solution Approach 2:
The patent segments the stylization process into distinct computational stages: (1) feature extraction from the input image, (2) style representation learning from reference images, and (3) feature fusion to generate the stylized output. This segmentation allows each stage to be optimized independently, improving overall stability
2Reliability
If video segmentation networks are used to enforce temporal consistency, then reliability is improved, but device complexity and memory requirements increase significantly
Solution Approach 1:
The patent uses a lightweight optical flow estimation approach that copies and adapts motion vectors from previous frames rather than employing complex video segmentation networks. This copying strategy maintains temporal consistency by propagating segmentation information through time with minimal computational overhead
Solution Approach 2:
The system dynamically adjusts the temporal consistency enforcement based on scene complexity and motion magnitude. For static or low-motion scenes, minimal temporal constraints are applied, while for high-motion scenes, adaptive optical flow guidance is activated, optimizing the balance between reliability and complexity
3Reliability
If both original and styled assets are stored in memory, then reversibility is improved, but storage requirements double
Solution Approach 1:
The patent introduces an intermediary compressed representation that stores only the essential transformation parameters needed for reversibility. Instead of storing the complete original asset, the system stores a compact set of style transfer parameters, feature map differences, or latent space coordinates that can reconstruct the original image when applied to the styled output
Solution Approach 2:
The system transitions from storing pixel-level data to storing parameter-level representations. By encoding the relationship between original and styled images in terms of transform parameters, feature statistics, or latent vectors, the storage requirement is dramatically reduced while maintaining the ability to reversibly reconstruct the original asset
4Productivity
If thumbnail-based transformation learning is used, then processing speed is improved, but image quality may be compromised
Solution Approach 1:
The patent implements a nested processing architecture where thumbnail-based transformation learning is nested within a full-resolution refinement framework. The thumbnail stage learns the coarse transformation parameters quickly, which are then applied as initial guidance for a second-stage processing that operates on the full-resolution image to preserve fine details and maximize output quality
Data Source
AI summary
This disclosure relates generally to the field of photography, videography and digital graphics. More particularly, but not by way of limitation, it relates to a camera control system and image processing system, which can take an input asset (e.g., a still image, a video, or a still image with an associated video) that is a realistic rendering of a scene (i.e., an “unstyled” version) and output an asset rendered with a particular aesthetic style to match a particular artistic intent (i.e., a “stylized” version). Advantageously, the asset can then be reversibly un-styled (or re-styled) accurately—without also storing a full size “unstyled” (i.e., original) version of the asset.