Node-Based Image Colorization Workflow
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Solution Overview
Problem
Conventional image/video editing methods require manual selection and colorization of regions in each frame of a video, making the process time-consuming and cumbersome for users.
Innovation Solution
A node-based image/video editing system that utilizes a neural network-based colorization model, allowing users to select grayscale images and apply color effects through a workflow, eliminating the need for manual region selection in every frame by using auto-style or user-guided colorization options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual region selection and colorization is performed for each frame, then colorization precision is improved, but time consumption increases significantly
Solution Approach 1:
The system performs preliminary action by automatically selecting regions of interest and generating colorization masks for all frames in advance, using neural network-based object detection and tracking algorithms. This preliminary automated region selection eliminates the need for manual per-frame selection while maintaining colorization quality, thereby resolving the contradiction between precision and time consumption.
Solution Approach 2:
The system uses copying by generating colorization masks that can be applied across multiple frames. Once a region is identified in one frame, the mask can be copied and adapted to subsequent frames through temporal tracking, significantly reducing the time required for colorization while maintaining consistency across the video sequence.
2Manufacturing precision
If manual colorization is performed on every frame, then colorization quality is improved, but user effort increases
Solution Approach 1:
The system implements self-service by automatically performing region selection, mask generation, and colorization application without requiring manual user intervention for each frame. The neural network-based automated system serves itself by identifying objects, creating masks, and applying colorization effects, thereby maintaining high colorization quality while dramatically reducing user effort to simple parameter configuration and final review.
3Productivity
If automated colorization is applied to entire video sequences, then productivity is improved, but control precision decreases
Solution Approach 1:
The system incorporates feedback mechanisms that allow users to review and adjust automatically generated colorization masks before final application. The feedback loop enables users to correct any imprecisions in automated region selection while maintaining the high productivity benefits of automation, thus resolving the contradiction between speed and precision by allowing iterative refinement of results.
Data Source
AI summary
A system and method for node-based image colorization is provided. The system receives a first user input via a Graphical User Interface (GUI) of an image/video editing application and controls a display device to display a node graph on the GUI based on the first user input. The node graph includes an input node to select grayscale images and a colorization node which may represent a workflow for colorization of at least a first object in the grayscale images. The system receives a second user input associated with a setting of the colorization node and selects an image colorization model based on the second user input. The system executes the workflow associated with the colorization node to transmit the grayscale images to a computing device that hosts the selected image colorization model, and to receive colorized images including at least the first object colorized based on a color effect.