Video Super-Resolution Network for Compression-Quality Tradeoffs
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Solution Overview
Problem
Existing video compression technologies face challenges in balancing data amount and picture quality, with either low complexity leading to insignificant quality improvement or high complexity resulting in significant picture quality and rate degradation.
Innovation Solution
A video processing method utilizing an SR network model with a first sub-network model for resolution improvement and a second sub-network model for quality enhancement, applied in pre- and post-processing stages to enhance both resolution and picture rate, thereby improving the quality and efficiency of video compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If video compression is applied to reduce data amount, then storage and transmission efficiency is improved, but picture quality is degraded
Solution Approach 1:
The video processing system is segmented into multiple processing stages: initial compression, super-resolution enhancement, and quality improvement. Each stage handles specific aspects of the video data, allowing optimization of both compression ratio and picture quality independently. The super-resolution module processes only the essential high-frequency components, reducing the overall data amount while maintaining quality.
Solution Approach 2:
The patent applies parameter changes by transforming the video from frequency domain to spatial domain and back, adjusting the resolution parameters through super-resolution algorithms. The system dynamically adjusts compression parameters based on the importance of different video components, preserving critical quality parameters while reducing data amount in less critical areas.
2Productivity
If simple compression algorithms are used, then coding efficiency is improved, but quality improvement effect is insignificant
Solution Approach 1:
The compression system is divided into multiple functional modules: a fast initial compression module for high coding efficiency, and a separate super-resolution enhancement module for quality improvement. This segmentation allows the system to achieve both high coding efficiency in the first stage and significant quality improvement in the second stage, avoiding the need to choose between the two.
Solution Approach 2:
The patent applies preliminary compression before super-resolution enhancement. By first applying a efficient compression algorithm to reduce data amount, and then applying super-resolution to the compressed video, the system achieves both coding efficiency and quality improvement. The preliminary compression removes redundant data, and the subsequent enhancement restores and improves critical quality aspects.
3Manufacturing precision
If complex quality improvement algorithms are applied, then picture quality is significantly improved, but algorithm complexity becomes too high
Solution Approach 1:
The quality improvement process is segmented into distinct modules: super-resolution enhancement, artifact reduction, and detail preservation. Each module handles a specific aspect of quality improvement with optimized complexity. The super-resolution module uses efficient interpolation algorithms, while artifact reduction applies targeted filtering only where needed, reducing overall algorithm complexity compared to applying a single complex algorithm to the entire video.
Solution Approach 2:
The patent applies local quality processing by identifying regions of the video that require quality improvement and applying enhancement algorithms only to those specific areas. Instead of processing the entire video uniformly with high complexity, the system analyzes the video content and applies appropriate processing strength and complexity locally, reducing overall algorithm complexity while maintaining picture quality in critical regions.
4Manufacturing precision
If video resolution is increased, then picture quality is improved, but data amount increases
Solution Approach 1:
The patent applies parameter changes through super-resolution algorithms that generate high-resolution video from low-resolution input by predicting and synthesizing missing high-frequency details. Instead of simply upsampling which would increase data amount proportionally, the system uses learned patterns and correlations to reconstruct detailed information, achieving high resolution with minimal additional data by efficiently representing the enhanced video.
Data Source
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AI summary
Disclosed in embodiments of the present application are a video processing method and apparatus, and a device, a decoder, a system and a storage medium, applied to a video device. The method comprises: obtaining a video sequence of a first resolution, the video sequence comprising at least one video frame; and inputting the video sequence into a super-resolution network model to obtain a target video sequence of a second resolution, wherein the super-resolution network model at least comprises a first sub-network model and a second sub-network model, the first sub-network model is used for improving the resolution of the video sequence, and the second sub-network model is used for improving the quality of at least one image frame in the output result of the first sub-network model.