Video Super-Resolution Segmentation for Adaptive Coding Efficiency
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Solution Overview
Problem
Existing video coding technologies lack flexibility in applying super-resolution processes, as they typically use fixed methods for all frames, which can lead to suboptimal compression efficiency.
Innovation Solution
Apply different super-resolution processes to different sub-regions of a video unit, utilizing a combination of neural network-based and non-neural network-based methods, with customizable parameters and indications in the bitstream for optimal selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed up-sampling methods are applied to all frames in a video sequence, then the processing is simple and consistent, but the compression efficiency and quality are limited due to inability to adapt to varying content and resolution needs
Solution Approach 1:
The video unit is divided into multiple sub-regions, and different super-resolution processes are applied to different sub-regions. This segmentation allows the system to adapt to varying content characteristics within different regions while maintaining manageable processing complexity through localized operations.
Solution Approach 2:
The up-sampling process is made dynamic by selecting different super-resolution processes (neural network-based or non-neural network-based) based on decoded information and reference sub-regions. This dynamic adaptation enables the system to optimize compression efficiency and quality for each specific content type while managing complexity through conditional application.
2Productivity
If different super-resolution processes are applied to different sub-regions, then compression efficiency and quality are improved through adaptive up-sampling, but the processing complexity and computational burden increase
Solution Approach 1:
Different super-resolution processes are applied to different sub-regions based on their specific content characteristics. This local quality approach allows optimization of compression efficiency and quality for each region while managing overall complexity through targeted application rather than uniform processing.
Solution Approach 2:
The selection of super-resolution processes is based on decoded information and reference sub-regions, creating a feedback mechanism that guides the up-sampling process. This feedback-driven selection optimizes compression efficiency while managing complexity by using previously decoded information to inform subsequent processing decisions.
3Measurement precision
If neural network-based SR processes are used, then up-sampling quality is improved, but computational resources and processing time are increased
Solution Approach 1:
Neural network-based SR processes are applied selectively to sub-regions where they provide the most benefit, rather than uniformly to all regions. This partial application approach improves up-sampling quality for critical regions while reducing overall computational resource consumption compared to full application.
Solution Approach 2:
The system can switch between different SR processes (neural network-based or non-neural network-based) based on content characteristics and computational constraints. This parameter change approach allows optimization of quality for regions where it matters most while managing computational resources by using lighter methods where appropriate.
Data Source
AI summary
A method of processing video data. The method includes applying different super resolution (SR) processes to different sub-regions of a video unit, and performing a conversion between a video including the different regions of the video unit and a bitstream of the video based on the different SR processes as applied. A corresponding video coding apparatus and non-transitory computer-readable recording medium are also disclosed.