Neural Network Post-Filter Energy Adaptation for Video QoE
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video coding systems fail to efficiently adapt to energy consumption changes, leading to inefficient energy use and quality of experience.
Innovation Solution
Implementing a neural network post-filter for energy adaptation using a neural-network post-filter characteristic (NNPFC) SEI message to adjust energy consumption based on quality of experience (QoE) and user profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If video coding systems use traditional compression methods, then storage and transmission bandwidth are reduced, but energy consumption cannot be efficiently adapted to different quality requirements
Solution Approach 1:
The system dynamically adapts video quality by adjusting the neural network post-filter processing level based on real-time energy availability and quality requirements. The decoder can switch between different processing modes (full neural network processing, reduced processing, or no processing) to balance energy consumption against quality of experience, making the system flexible rather than static
Solution Approach 2:
The patent changes key parameters including the neural network processing intensity, filter application level, and quality metric thresholds to optimize the balance between energy consumption and video quality. By adjusting these parameters based on energy adaptation indications, the system can operate efficiently across different energy scenarios while maintaining acceptable quality
2Reliability
If neural network post-filter is applied to improve video quality, then quality of experience increases, but energy consumption increases
Solution Approach 1:
Instead of always applying full neural network post-filter processing, the system applies partial processing only when energy is available and quality improvement is needed. The energy adaptation mechanism allows the decoder to apply the neural network filter at reduced intensity or skip it entirely during energy-constrained periods, achieving acceptable quality with less energy expenditure
Solution Approach 2:
The system uses feedback from energy adaptation indications and quality metric evaluations to dynamically control neural network post-filter application. The decoder monitors energy availability and quality requirements, then adjusts the level of neural network processing accordingly, creating a closed-loop system that balances quality and energy consumption
3Reliability
If video quality is maintained at high levels, then quality of experience is improved, but energy waste increases
Solution Approach 1:
The system applies quality enhancement selectively rather than uniformly across all video content. The neural network post-filter is applied only to specific regions or time periods where quality improvement provides the most value, while reducing or eliminating processing in areas where high quality is less critical, thereby reducing overall energy waste while maintaining acceptable video quality
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
Systems, methods, and instrumentalities are disclosed associated with neural network post filter for energy adaptation. Messages associated with energy adaptation operations may be signaled and/or used. The messages may be associated with neural network post-filter characteristics (NNPFC) and/or supplemental enhancement information (SEI). The message may be an NNPFC SEI message. Adaptation in terms of energy consumption of content (e.g., decoded content) may be performed, for example, based on a received NNPFC SEI message. An NNPFC SEI message may enable reduction of energy consumption of a content while used. An NNPFC SEI message may enable increase of energy consumption of a content, for example, if the increase in energy is associated with an increased quality of experience (QoE) associated with the content.