Video Processing Semantic Importance Resolution Enhancement
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Solution Overview
Problem
Current video processing techniques face challenges in efficiently enhancing image resolution while balancing computational complexity and quality, particularly in applying deep learning-based methods that are resource-intensive and unsuitable for real-time applications like video transcoding and streaming.
Innovation Solution
The method involves determining the semantic importance of video frames and applying resolution-enhancement techniques based on spatial or temporal importance, using complex DNN-based methods for critical regions and less complex interpolation-based techniques for less important areas, thereby optimizing computational resources and maintaining high image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If deep learning-based resolution enhancement methods are applied to entire video frames, then image quality is improved, but computational complexity and resource consumption increase significantly
Solution Approach 1:
The patent applies different processing strategies to different regions of the video frame based on their importance. Important regions (containing semantic information) receive deep learning-based resolution enhancement, while less important regions use simpler interpolation methods. This local differentiation maintains image quality where needed while reducing overall computational complexity.
Solution Approach 2:
The video frame is segmented into important and less important regions based on semantic information detection. This segmentation allows the system to apply computationally intensive deep learning methods only to critical regions rather than the entire frame, thereby reducing total resource consumption while preserving essential image quality.
2Manufacturing precision
If deep learning-based resolution enhancement is applied to video sequences, then resolution is improved, but processing time and computational resources increase
Solution Approach 1:
The system selectively applies high-resolution deep learning enhancement only to important regions of the video frame rather than processing the entire frame at full resolution. This local quality approach maintains acceptable resolution in critical areas while significantly reducing processing time and computational resource requirements.
Solution Approach 2:
Instead of applying full deep learning-based resolution enhancement to the entire video frame, the system performs partial action by processing only the important regions with deep learning methods. The remaining less important regions are handled with simpler, faster interpolation techniques, achieving a balance between resolution improvement and processing efficiency.
3Manufacturing precision
If uniform resolution enhancement is applied to all regions, then overall image quality is improved, but computational resources are wasted on less important areas
Solution Approach 1:
The patent implements local quality by detecting semantically important regions and applying high-quality deep learning-based resolution enhancement only to those areas. Less important regions receive simpler processing, thereby avoiding waste of computational resources while maintaining overall image quality where it matters most.
Solution Approach 2:
The system dynamically changes processing parameters based on regional importance. Important regions receive intensive deep learning processing with high computational parameters, while less important regions use reduced parameters with simpler interpolation methods. This adaptive parameter adjustment optimizes the balance between image quality and computational resource utilization.
Data Source
AI summary
Methods and apparatuses for video processing based on spatial or temporal importance include: in response to receiving picture data of a picture of a video sequence, determining a level of semantic importance for the picture data, the picture data including a portion of the picture; and applying to the picture data a first resolution-enhancement technique associated with the level of semantic importance for increasing resolution of the picture data, wherein the first resolution-enhancement technique is selected from a set of resolution-enhancement techniques having different computational complexity levels.