Object-Based Temporal Video Pre-Processing for Adaptive Image Quality
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Solution Overview
Problem
Current image processing methods for video frames in raw format lack adaptiveness, leading to suboptimal image quality due to uniform processing parameters across different regions, which can result in over-enhancement or unnatural color representation, especially in scenes with varying objects and lighting conditions.
Innovation Solution
The method involves object-based temporal pre-processing, where video frames are processed separately for regions corresponding to the same object, using adaptive image processing techniques such as de-noising with parameters determined based on object-specific features and capturing conditions, and employing clustering methods like K-means to group image regions with similar features across frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If uniform processing parameters are applied across all image regions, then processing simplicity is maintained, but image quality deteriorates due to over-enhancement or unnatural color representation
Solution Approach 1:
The image is divided into multiple regions based on object detection and clustering. Each region corresponds to a specific object or background area, allowing different processing parameters to be applied to each region. This segmentation enables adaptive processing that improves image quality while maintaining reasonable processing complexity through automated region classification.
Solution Approach 2:
Different image processing parameters are applied to different regions of the image based on their classification (foreground objects vs. background). For example, denoising strength, sharpening, and color correction parameters are locally adapted to each region's characteristics, preventing over-enhancement in uniform areas while preserving detail in important objects.
2Manufacturing precision
If adaptive processing is applied to each object separately, then image quality improves through individualized processing, but processing time increases due to separate processing of multiple regions
Solution Approach 1:
Object detection and region clustering are performed as preliminary steps before the actual image processing. By pre-segmenting the image into distinct regions and classifying them as foreground or background, the system prepares the structure for adaptive processing in advance, allowing parallel or optimized processing of multiple regions simultaneously rather than sequentially.
Solution Approach 2:
Multiple image processing operations (denoising, sharpening, color correction, contrast enhancement) are combined into a unified region-based processing framework. This allows the system to process multiple operations across multiple regions in an integrated manner, reducing overall processing time compared to handling each operation separately for each region.
3Manufacturing precision
If region-based processing is implemented, then color accuracy improves through object-specific processing, but processing complexity increases due to multiple processing steps
Solution Approach 1:
A universal processing framework is implemented that handles multiple image processing tasks (denoising, sharpening, color correction, contrast enhancement) within a single region-based system. The same basic processing pipeline is applied to all regions, but with locally adapted parameters, providing multi-functionality without requiring separate complex processing chains for each operation.
Solution Approach 2:
The system changes processing parameters locally for different regions rather than implementing fundamentally different processing algorithms. By adjusting parameters such as denoising strength, sharpening intensity, and color correction factors based on region classification, the system achieves object-specific processing with minimal increase in overall system complexity.
Data Source
AI summary
The present disclosure relates to pre-processing of video images. In particular, the video images are pre-processed in an object-based manner, i.e., by applying different pre-processing to different objects detected in the image. Moreover, the pre-processing is applied to a group of images. As such, object detection is performed in a plurality of images and the pre-processing for the plurality of images may be adapted to the decoded images and is applied to the decoded images.