Scene-Based Video Enhancement Algorithm Selection
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Solution Overview
Problem
Existing video enhancement technologies often result in video picture deformation, artifacts, and reduced video viewing experience due to their limitations in adapting to diverse video content styles and complex motion and illumination changes.
Innovation Solution
A method and apparatus for video enhancement that involves segmenting a target video into groups of images based on scenes, determining a matched video enhancement algorithm for each group using a trained quality assessment model, and performing video enhancement processing on each group using the matched algorithm, followed by splicing the results to obtain enhanced video data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single video enhancement algorithm is used for all video scenes, then the device complexity is reduced, but the adaptability to different video content styles and scenes deteriorates
Solution Approach 1:
The video is segmented into multiple groups of images based on scene boundaries detected by the quality assessment model. Each group is then processed by a specific enhancement algorithm selected to match its content characteristics, rather than applying a single algorithm to the entire video. This segmentation approach resolves the contradiction by dividing the video processing task into scene-specific segments.
Solution Approach 2:
The system dynamically selects different enhancement algorithms for different video scenes based on real-time quality assessment. The quality assessment model continuously evaluates video content characteristics and adjusts the enhancement algorithm selection accordingly, making the system adaptive to changing video content rather than static.
2Productivity
If video enhancement is performed on the entire video using a single algorithm, then the processing efficiency is improved, but the video enhancement quality deteriorates due to lack of scene-specific optimization
Solution Approach 1:
The video is divided into multiple groups of images based on scene boundaries. Each group is processed independently with an algorithm optimized for its specific content characteristics, improving enhancement quality while maintaining reasonable processing efficiency through parallelizable independent group processing.
Solution Approach 2:
Different enhancement algorithms are applied to different local regions (scene groups) of the video based on their specific content characteristics. This local quality approach ensures that each scene group receives the most appropriate enhancement treatment, improving overall video enhancement quality compared to uniform processing.
3Adaptability or versatility
If multiple video enhancement algorithms are applied to different video scenes, then the adaptability to different content styles is improved, but the device complexity increases
Solution Approach 1:
A quality assessment model is introduced as an intermediary between the video input and enhancement algorithms. This model automatically evaluates video content characteristics and selects appropriate enhancement algorithms, reducing the perceived system complexity by automating the selection process rather than requiring manual configuration or complex user decisions.
Solution Approach 2:
The system performs self-service by automatically assessing video content quality and selecting appropriate enhancement algorithms without external intervention. The quality assessment model autonomously analyzes video characteristics and makes algorithm selection decisions, reducing the operational complexity for users.
4Manufacturing precision
If scene-based segmentation and algorithm matching is implemented, then the video enhancement quality is improved, but the processing time increases due to multiple processing steps
Solution Approach 1:
The video is segmented into scene groups that can be processed independently and potentially in parallel. This segmentation allows for optimized processing where each group is handled by the most suitable algorithm, improving quality while the independent nature of groups enables parallel processing to mitigate time increases.
Solution Approach 2:
Scene boundary detection and quality assessment are performed as preliminary actions before the actual enhancement processing. By identifying scene groups in advance, the system can pre-determine which algorithms to apply to each group, streamlining the overall processing flow and reducing the time penalty of multiple processing steps.
Data Source
AI summary
The present disclosure discloses a video enhancement method and apparatus. The method includes: segmenting a target video into a plurality of groups of images, the images in the same group belonging to the same scene; determining, for each group of images, a matched video enhancement algorithm using a pre-trained quality assessment model, and performing video enhancement processing on the each group of images using the video enhancement algorithm; and sequentially splicing video enhancement processing results of all groups of images to obtain video enhancement data of the target video. With the present disclosure, the video enhancement processing effect can be improved and the video viewing experience can be improved.