RPR-Based Super-Resolution With Wavelet Decomposition for Edge Recovery
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Solution Overview
Problem
Existing video coding technologies face challenges in transmitting high-definition videos with limited bandwidth due to the significant increase in video data, leading to transmission and storage issues, and traditional resampling-based methods fail to reconstruct clear and accurate edge information, especially for videos with complex characteristics.
Innovation Solution
A CNN filter is used for up-sampling processes, incorporating LR reconstructed and predicted frames, with wavelet decomposition and spatial and channel attention mechanisms to enhance edge information recovery, and residual blocks to prevent information loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional RPR up-sampling with simple interpolation method is used, then transmission bandwidth is reduced, but edge information reconstruction is inaccurate and blocking artifacts appear
Solution Approach 1:
The patent replaces traditional mechanical interpolation methods with a deep learning-based CNN model. The network learns complex mapping relationships between low-resolution and high-resolution frames, substituting simple mathematical interpolation with intelligent pattern recognition to accurately reconstruct edge information while maintaining bandwidth efficiency.
Solution Approach 2:
The patent combines multiple input components (down-sampled current frame, reference frames, prediction information) into a composite input structure for the CNN. This composite approach integrates diverse information sources to enhance edge reconstruction accuracy without proportionally increasing transmission bandwidth.
2Measurement precision
If high-definition video resolution and frame rates are increased, then visual experience is improved, but video data amount significantly increases causing transmission and storage challenges
Solution Approach 1:
The patent segments the video processing into distinct stages: down-sampling at encoding, efficient transmission of reduced data, and intelligent up-sampling at decoding. This segmentation allows quality preservation while reducing data transmission requirements by processing different resolutions at different stages of the video pipeline.
Solution Approach 2:
The patent performs down-sampling as a preliminary action before encoding and transmission. By reducing resolution beforehand, the system minimizes the data amount that needs to be transmitted and stored, while the subsequent up-sampling restoration recovers the visual quality, effectively decoupling transmission requirements from final quality output.
3Measurement precision
If CNN filter with wavelet decomposition and attention mechanisms is used, then edge information recovery is enhanced, but computational complexity increases
Solution Approach 1:
The patent segments the CNN architecture into specialized components: wavelet decomposition modules for multi-scale analysis, spatial attention mechanisms for location-specific processing, and channel attention for feature-specific refinement. This segmentation allows each component to focus on specific aspects of edge recovery, improving efficiency despite the overall increased complexity.
Solution Approach 2:
The patent introduces wavelet decomposition to transform the input space into multiple frequency sub-bands, adding a frequency dimension to the processing. This dimensional transformation enables the network to capture edge information at different scales and frequencies, enhancing recovery capability while organizing complexity in a structured manner.
Data Source
AI summary
A method for video processing includes receiving an input image via an initial feature extraction portion, where the initial feature portion includes a result of RPR up-sampling, a low resolution (LR) reconstructed frame, and an LR predicted frame; processing the input image by a first convolution layer; and processing the input image by a multi-level semantic information mapping portion to generate a combined image, where the multi-level semantic information mapping portion includes one or more residual spatial and channel attention blocks (RSCBs). Each of the RSCBs includes two or more residual spatial blocks (RSBs) and a channel attention (CA) block. Each of the RSBs includes a spatial attention (SA) block.