N-Level Encoder Decoder Video Frame Prediction
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Solution Overview
Problem
Existing video prediction techniques struggle with accurately predicting future frames due to the uncertainty of future events and the unpredictability of spatio-temporal dynamics, leading to low accuracy and difficulty in considering changes in objects and motions within videos.
Innovation Solution
The proposed solution involves an N-level encoder that extracts latent representations from video frames passed through multiple layers in a deep learning neural network, a feature learning unit that obtains predicted feature data, and an N-level decoder that generates predicted video frames based on the predicted feature data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional video prediction techniques are used, then the system is simple to implement, but the prediction accuracy is low due to uncertainty of future events and unpredictability of spatio-temporal dynamics
Solution Approach 1:
The patent divides the video prediction system into multiple levels (N-level encoder, N-level decoder) with different resolution layers. Each level processes video frames at different granularities, allowing the system to capture both global context and detailed local information, thereby improving prediction accuracy while maintaining manageable complexity through modular architecture
Solution Approach 2:
The patent introduces a multi-resolution dimension by processing video frames at N different levels of detail. The encoder extracts latent representations at multiple resolutions, and the decoder reconstructs predictions at corresponding levels. This dimensional expansion allows the system to handle spatio-temporal dynamics more effectively, addressing the unpredictability challenge
2Measurement precision
If deep learning neural network with multiple layers is used, then the prediction becomes more accurate and vivid, but the computational resources and processing time increase
Solution Approach 1:
The neural network is segmented into N levels with each level handling specific resolution requirements. The encoder processes input frames through N layers to extract latent representations, and the decoder reconstructs predictions through corresponding layers. This segmentation allows computational resources to be allocated efficiently across different resolution levels, reducing overall computational burden while maintaining high prediction accuracy
Solution Approach 2:
The system applies deep learning only where necessary by using N-level processing for the most critical prediction tasks while potentially using simpler methods for less critical aspects. The multi-level architecture allows selective application of computational intensity based on the specific prediction requirements, optimizing the balance between accuracy and computational resource consumption
3Reliability
If conventional prediction techniques are used, then the processing time is shorter, but the prediction fails to consider changes in objects and motions in the video
Solution Approach 1:
The N-level encoder performs preliminary extraction of latent representations from input video frames before the prediction task. By pre-processing and encoding the input frames at multiple levels, the system prepares feature data that can be quickly used for prediction, reducing the overall processing time while improving reliability through comprehensive feature extraction
Solution Approach 2:
The patent maintains continuous processing through the N-level encoder-decoder architecture where each level continuously refines the prediction. The encoder continuously extracts features and the decoder continuously generates predictions at different resolutions, ensuring that object changes and motions are captured continuously rather than in discrete steps, thereby improving reliability without significant time penalty
Data Source
AI summary
An apparatus and a method of predicting a video frame are provided. The apparatus includes a level encoder configured to extract and learn at least one feature from a video frame, a feature learning unit configured to learn based on the at least one feature or transmit predicted feature data corresponding to the at least one feature, and a level decoder configured to obtain and learn a predicted video frame based on the predicted feature data.