Compression-Informed Video Super-Resolution for Artifact-Robust Frames
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Solution Overview
Problem
Existing video super-resolution methods perform poorly on compressed video data due to the lack of consideration for compression artifacts, leading to significant artifacts and reduced performance.
Innovation Solution
A bi-directional training approach for a machine-learned video super-resolution model that includes a bi-directional recurrent module, detail-aware flow estimation, and Laplacian enhancement module to address compression artifacts in video frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing VSR models are trained on compressed frames to bring additional compression information, then the model can handle compressed video data, but the performance deteriorates without specific changes to network module designs
Solution Approach 1:
The network is divided into separate modules: a denoising module to handle compression artifacts and a VSR module to perform super-resolution. This segmentation allows each module to be optimized for its specific function, with the denoising module preparing compressed frames and the VSR module processing the cleaned data, thereby maintaining high performance on compressed video data.
Solution Approach 2:
A denoising module is introduced as an intermediary between the compressed frame input and the VSR model. This intermediary component specifically addresses compression artifacts by removing noise and artifacts from the compressed frames before they are processed by the VSR module, enabling the VSR model to maintain its performance on compressed data.
2Manufacturing precision
If a denoising model is applied to remove compression artifacts before VSR processing, then high-quality frames are provided to the VSR model, but the final performance deteriorates due to changed degradation kernels
Solution Approach 1:
The denoising module performs preliminary action by removing compression artifacts from the input frames before they are fed to the VSR model. This preliminary processing cleans the data and prepares it for optimal processing by the VSR module, ensuring that the degradation kernel remains consistent with the training data distribution and maintaining high super-resolution performance.
3Reliability
If VSR models are trained on uncompressed data to achieve high performance, then excellent super-resolution results are obtained, but significant artifacts are generated when operated on heavily compressed input videos
Solution Approach 1:
The system converts the harmful compression artifacts into a manageable problem by introducing a denoising module that specifically targets and removes these artifacts. The denoising module learns to identify and eliminate compression-related noise and artifacts, transforming the previously harmful compressed input into clean data that the VSR module can process effectively, thereby enabling high performance on compressed video data.
Data Source
AI summary
Example aspects of the present disclosure are directed to systems and methods which feature a machine-learned video super-resolution (VSR) model which has been trained using a bi-directional training approach. In particular, the present disclosure provides a compression-informed (e.g., compression-aware) super-resolution model that can perform well on real-world videos with different levels of compression. Specifically, example models described herein can include three modules to robustly restore the missing information caused by video compression. First, a bi-directional recurrent module can be used to reduce the accumulated warping error from the random locations of the intra-frame from compressed video frames. Second, a detail-aware flow estimation module can be added to enable recovery of high resolution (HR) flow from compressed low resolution (LR) frames. Finally, a Laplacian enhancement module can add high-frequency information to the warped HR frames washed out by video encoding.