Optical Flow Video Compression for Artifact-Reduced Reconstruction
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Solution Overview
Problem
Existing video compression methods are inefficient and often result in unnatural artifacts during decompression, particularly when dealing with smooth and predictable motion between frames.
Innovation Solution
A neural network-based compression system that utilizes optical flow data and residual frames to compress video, employing autoencoder and generative adversarial neural networks to generate and encode optical flow and residual data efficiently, ensuring high-fidelity reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional video compression methods are used, then storage requirements are reduced, but video quality deteriorates with unnatural artifacts appearing during decompression
Solution Approach 1:
The patent segments video compression into two distinct components: optical flow representation and residual frame representation. The optical flow captures motion patterns between frames, while the residual captures differences not explained by motion. This segmentation allows each component to be optimized independently, reducing artifacts while maintaining compression efficiency.
Solution Approach 2:
The patent changes the representation parameters from traditional pixel-based differences to motion-based optical flow vectors. By representing video data in terms of motion fields and residuals rather than direct pixel differences, the compression achieves better quality at comparable bitrates, particularly for smooth motion sequences.
2Quantity of substance
If compression ratios are increased, then storage requirements are reduced, but video reconstruction quality deteriorates
Solution Approach 1:
The patent introduces optical flow as an intermediary representation that mediates between the reference frame and the current frame. Instead of directly compressing pixel differences, the system uses optical flow vectors as an intermediate step to describe motion, then compresses the residual. This intermediary representation preserves motion information more efficiently, maintaining reconstruction quality at higher compression ratios.
3Productivity
If traditional compression algorithms are used, then processing speed is maintained, but compression efficiency deteriorates
Solution Approach 1:
The patent replaces traditional mechanical compression algorithms with a neural network-based system. The neural network learns optimal compression strategies during training and automatically applies them during encoding, achieving superior compression efficiency without requiring complex manual tuning of compression parameters.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compressing video data. In one aspect, a method comprises: receiving a video sequence of frames; generating, using a flow prediction network, an optical flow between two sequential frames, wherein the two sequential frames comprise a first frame and a second frame that is subsequent the first frame; generating from the optical flow, using a first autoencoder neural network: a predicted optical flow between the first frame and the second frame; and warping a reconstruction of the first frame according to the predicted optical flow and subsequently applying a blurring operation to obtain an initial predicted reconstruction of the second frame.