Image-to-Video Conversion Using Flow and Residual Synthesis
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Solution Overview
Problem
Existing technologies struggle to convert still images into high-quality videos while maintaining detail and image quality without artifacts.
Innovation Solution
An electronic device employing an image processing module with a neural network architecture, including a feature extraction unit, flow generation unit, residual generation unit, and residual synthesis unit, to transform still images into videos by generating frame images with maintained detail and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a still image is converted into a video using existing technologies, then the conversion process is simple, but the video quality deteriorates with artifacts and loss of detail
Solution Approach 1:
The patent segments the image processing into multiple distinct modules: feature extraction unit, flow generation unit, residual generation unit, and residual synthesis unit. Each module performs a specific function in the transformation pipeline, allowing precise control over different aspects of image-to-video conversion while maintaining overall simplicity.
Solution Approach 2:
The patent introduces intermediary components such as flow information (optical flow) and residual information as mediators between the input still image and output video frames. These intermediaries enable the transformation process to maintain quality by capturing motion dynamics and refining details through multiple processing stages.
2Adaptability or versatility
If the still image is transformed into video frames, then motion information can be added, but structural and textural details are lost
Solution Approach 1:
The patent performs preliminary feature extraction from the still image before generating motion. By extracting and storing feature information (edges, corners, textures) in advance, the system can reference these features during video frame generation to ensure that motion transformation does not compromise structural and textural details.
Solution Approach 2:
The residual generation unit creates residual information by comparing transformed images with the original still image features. This residual feedback loop allows the system to identify and correct any loss of detail or structural integrity during transformation, ensuring high-quality output while maintaining motion capabilities.
3Manufacturing precision
If neural network processing is applied to convert still image to video, then image quality is maintained, but processing time increases
Solution Approach 1:
The patent divides the complex neural network processing into separate functional units (feature extraction, flow generation, residual generation, residual synthesis). This segmentation allows for optimized processing at each stage, enabling quality maintenance while reducing overall processing time through specialized operations rather than monolithic processing.
Solution Approach 2:
The patent employs parameter changes in the neural network architecture, using adaptive parameters that can be adjusted based on input characteristics. This allows the system to optimize processing speed and quality dynamically, reducing processing time when possible while maintaining high image quality when needed.
Data Source
AI summary
An electronic device configured to convert a still image into a video, includes: memory in which one or more instructions are stored; and at least one processor, wherein the one or more instructions, when executed by the at least one processor individually or collectively, cause the electronic device to: obtain flow information about a first image, obtain a plurality of transformation images obtained by transforming the first image, based on the flow information about the first image, obtain residual information about the plurality of transformation images, based on the plurality of transformation images and the first image, and generate a plurality of frame images, based on the plurality of transformation images and the residual information about the plurality of transformation images.