Semantic Region-Based Image Filtering for Lower Processing Overhead
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Solution Overview
Problem
Existing image processing technologies apply a single filter setting to an entire image, which is inefficient and resource-intensive, as they do not account for the varying characteristics and objects within different regions of an image.
Innovation Solution
An image processing arrangement that performs semantic analysis to identify distinct regions within an image and applies tailored filter settings to each region, optimizing image processing based on localized semantic knowledge of the scene and objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single filter setting is applied to the entire image, then the image processing is simple and fast, but the image quality is compromised because it does not account for varying characteristics in different regions
Solution Approach 1:
The patent divides the image into multiple regions based on semantic analysis of objects and scenes. Each region is then processed with tailored filter settings appropriate to its content (e.g., different filters for sky, water, land, or different objects within the image). This segmentation allows high-quality processing of each region while maintaining overall image quality without applying a single uniform filter throughout.
Solution Approach 2:
The patent implements local quality by applying different filter settings to different regions of the image based on their semantic characteristics. Instead of uniform processing, the system identifies regions containing specific objects or scenes and applies appropriate filters to each region, optimizing image quality locally while reducing unnecessary processing in other areas.
2Manufacturing precision
If multiple filter settings are applied to different regions, then the image quality is improved for each region, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent performs semantic analysis of the image to identify objects and regions before applying filters. This preliminary action classifies the image content and determines which regions require which filter settings, allowing the system to prepare processing parameters in advance and apply them efficiently during the actual filtering process, reducing overall processing time.
Solution Approach 2:
The patent applies partial action by selectively processing only the regions that require specialized filter settings based on semantic analysis. Rather than applying all possible filters to the entire image, the system identifies and processes only the necessary regions with appropriate filters, reducing computational overhead while maintaining quality where needed.
3Adaptability or versatility
If comprehensive semantic analysis is performed to identify regions and apply tailored filters, then the adaptability to different scenes is improved, but the computational complexity and processing resources required increase
Solution Approach 1:
The patent changes processing parameters dynamically based on semantic analysis results. The system identifies the type of objects or scenes in different regions and adjusts filter parameters accordingly (e.g., changing sharpness, color saturation, or noise reduction levels). This parameter adaptation enables high versatility across different scenes while managing computational complexity through targeted adjustments rather than exhaustive processing.
Data Source
AI summary
An image processing arrangement (100) comprising a controller (101) configured to: a) receive (410) image data; b) perform semantic analysis (430) on the image data to c) identify (432)) a first region (R1) and a second region (R2) and to d) select (434) first settings (S1) and second settings (S2) corresponding to the identified first region (R1) and second region (R2) respectively; e) adapt (440) at least one filter (316, 314) according to the selected first and second settings (S1, S2, Sx); and f) perform (450) image processing on image data based on the adapted at least one filter (316, 314) wherein the first settings (S1) are applied to the at least one filter (316, 314) for the first region (R1) and the second settings (S2) are applied to the at least one filter (316, 314) for the second region (R2).