3D Video Processing Model for Resolution and Duration Scaling
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Solution Overview
Problem
Existing image processing technologies struggle to efficiently generate images or videos with varying resolutions and durations, particularly in the context of model training, as conventional methods are limited to two-dimensional adjustments and face challenges in handling three-dimensional data effectively.
Innovation Solution
A video processing method utilizing a generative adversarial network with transposed 3D convolution units and multiple 3D convolutional layers, along with a second 3D convolutional layer for adjusting dimensions, to process input data into output video data with desired resolutions and durations, incorporating a 3D-patch extraction process to manage data complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional two-dimensional image processing methods are used, then the processing approach is simple, but the ability to handle three-dimensional video data with varying resolutions and durations is limited
Solution Approach 1:
The patent transitions from two-dimensional image processing to three-dimensional video processing by introducing the time dimension. The video processing model uses 3D convolutional layers that operate on volumetric data (height, width, time), enabling simultaneous processing of spatial and temporal information. This dimensional extension allows the model to handle video data with varying resolutions and durations effectively.
Solution Approach 2:
The video processing model is divided into multiple functional components: 3D convolutional layers for feature extraction, transposed 3D convolution units for resolution and duration adjustment, and separate processing pathways for spatial and temporal dimensions. This segmentation allows each component to specialize in specific tasks, improving overall adaptability while managing complexity through modular design.
2Manufacturing precision
If the video processing model uses multiple 3D convolutional layers and transposed convolution units, then the resolution and duration adjustment capability is improved, but the data processing complexity increases
Solution Approach 1:
The model adjusts video resolution and duration by dynamically changing parameters within the 3D convolutional layers and transposed convolution units. The transposed 3D convolution units use learnable parameters to control the scaling factors for resolution adjustment and duration adjustment, allowing precise control over output specifications without hardcoding specific transformation rules.
Solution Approach 2:
The patent introduces intermediate feature representations that act as mediators between the input video data and the final adjusted output. The 3D convolutional layers extract intermediate features that capture temporal and spatial patterns, which then feed into the transposed convolution units for resolution and duration adjustment. This intermediary step simplifies the overall transformation by breaking it into manageable processing stages.
3Loss of information
If the model processes entire video sequences, then the temporal information is preserved, but the computational burden and processing time increase
Solution Approach 1:
The video sequence is segmented into smaller temporal segments or frames that are processed independently or in small batches by the 3D convolutional layers. This segmentation reduces the computational burden of processing entire long videos at once while preserving temporal information through the temporal dimension of the 3D convolutions and the ability to reconstruct the full sequence from processed segments.
Solution Approach 2:
The model performs preliminary feature extraction using 3D convolutional layers that capture temporal patterns across multiple frames before applying resolution and duration adjustments. This preliminary action of extracting temporal features early in the processing pipeline preserves temporal information while reducing the complexity of subsequent processing steps, as the temporal structure is already encoded in the feature representations.
Data Source
AI summary
The present disclosure provides a video processing method, a video processing device, an electronic apparatus, and a readable storage medium. The video processing method includes: obtaining input data; and inputting the input data into a video processing model to obtain output video data. A resolution and/or a duration of the output video data is not equal to a resolution and/or a duration of the input data, the video processing model includes a plurality of generators arranged in sequence and corresponding to different image resolutions, each generator includes a transposed 3D convolution unit and a plurality of first 3D convolutional layers, the transposed 3D convolution unit is configured to generate first output data in accordance with the input data and intermediate processing data of the generator.