Time-Space-Frequency Tokens for Video Super-Resolution Artifacts
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Solution Overview
Problem
Existing video super-resolution (VSR) methods struggle to accurately distinguish between compression artifacts and physical textures, leading to inadequate image quality improvements in reconstructed high-resolution video.
Innovation Solution
A tokenization scheme that encodes frequency, spatial, and temporal data in tokens, utilizing a transformer network with a recurrent structure to propagate optical flow information, enhancing the accuracy of high-resolution image generation from low-resolution inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If video data is compressed to reduce file size and improve transmission/storage efficiency, then data quantity and transmission efficiency are improved, but image quality and detail accuracy are degraded
Solution Approach 1:
The patent segments the video data into individual frames and further divides each frame into patches, which are then tokenized into time-space-frequency tokens. This segmentation allows the model to process and reconstruct image details from compressed low-resolution data by analyzing local and temporal patterns separately, thereby improving recovered image quality without requiring the entire compressed video stream.
Solution Approach 2:
The patent transforms 2D image patches into 1D time-space-frequency tokens by encoding spatial and temporal information along with frequency components. This dimensional transformation enables the transformer model to capture complex relationships between frames and spatial locations, allowing high-quality reconstruction from compressed low-resolution input by leveraging multi-dimensional information fusion.
2Measurement precision
If existing VSR methods are used to reconstruct high-resolution video from low-resolution compressed video, then resolution is improved, but the ability to distinguish compression artifacts from physical textures remains insufficient
Solution Approach 1:
The patent introduces time-space-frequency tokens as an intermediary representation that bridges the gap between compressed low-resolution input and high-resolution output. These tokens encode multiple types of information (temporal, spatial, frequency) in a compressed form, allowing the transformer model to distinguish between real texture patterns and compression artifacts by analyzing their frequency and temporal characteristics before reconstruction.
Solution Approach 2:
The patent changes the representation parameters from standard pixel values to time-space-frequency tokens, which encode information in a transformed domain. This parameter transformation enables the model to perceive compression artifacts as distinct patterns from physical textures through frequency analysis, improving the reliability of artifact distinction while maintaining high resolution in the reconstructed output.
3Manufacturing precision
If a transformer network with recurrent structure is used to process time-space-frequency tokens, then image quality and detail prediction are improved, but computational complexity increases
Solution Approach 1:
The patent segments the video processing into independent frame-level operations, where each frame is processed separately through upsampling, patch division, and token generation. This segmentation allows the computationally intensive transformer model to process data in smaller batches, reducing overall computational complexity while maintaining high image quality through focused attention on relevant temporal and spatial patterns.
Solution Approach 2:
By transforming image data into time-space-frequency tokens, the patent reduces the effective dimensionality of the input to the transformer model. The tokens compress spatial and temporal information into a compact representation, which decreases the computational burden on the transformer while preserving the essential patterns needed for high-quality reconstruction.
Data Source
AI summary
A computing device including a processor configured to receive input video data including a plurality of input images. Each of the input images may include a plurality of input pixels. For each input image, the processor may be further configured to perform upsampling on the input image and divide the upsampled input image into a respective plurality of patches. For each patch, the processor may be further configured to generate a plurality of time-space-frequency tokens. The plurality of time-space-frequency tokens generated for the patch may be indexed by timestep, spatial location, and frequency. At least in part at a trained machine learning model, the processor may be further configured to generate a plurality of super-resolved output images based at least in part on the time-space-frequency tokens. The processor may be further configured to output the super-resolved output images.