Scene Adaptive Brightness Contrast Enhancement via Prototype Interpolation
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Solution Overview
Problem
Current brightness/contrast enhancement (BCE) methods in imaging and video processing fail to adequately address the complexity of human perception, often producing poor results due to their inability to adapt to diverse image content, particularly in scenes with flat objects or clouds in the sky.
Innovation Solution
The implementation of a BCE system that uses a set of prototypes to adapt brightness/contrast enhancement behavior to different scene types, with both global and local BCE methods, where global BCE operates on the entire image and local BCE operates on small areas, interpolating from a set of pre-generated prototypes to determine the best enhancement for each image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed BCE technique is applied to all images regardless of content, then the processing is simple and fast, but the enhancement quality deteriorates on diverse scene types
Solution Approach 1:
The patent segments the image into multiple regions (sky region, ground region, etc.) and applies different BCE techniques to each region based on its characteristics. This allows the system to maintain simple processing for uniform regions while applying adaptive enhancement only where needed, resolving the contradiction between processing speed and adaptability.
Solution Approach 2:
The patent implements local BCE that adapts enhancement parameters to specific image regions and content types. By analyzing local image characteristics and applying region-specific enhancement, the system achieves high adaptability to diverse content while maintaining efficiency through selective processing rather than uniform application across the entire image.
2Adaptability or versatility
If local BCE is implemented to operate on small areas with interpolation from prototypes, then the enhancement quality for diverse scenes improves, but the computational complexity increases
Solution Approach 1:
The patent pre-generates a set of BCE prototypes during an offline training phase, capturing various scene types and their optimal enhancement parameters. During actual image processing, the system only needs to match the input image to the nearest prototype and apply the corresponding enhancement, significantly reducing online computational complexity while maintaining high adaptability to diverse scenes.
Solution Approach 2:
The patent uses prototype copying where pre-computed enhancement patterns from training images are stored and reused for similar scene types. Instead of performing complex real-time analysis, the system copies and applies proven enhancement patterns from the prototype library, reducing computational complexity while preserving enhancement quality.
Data Source
AI summary
A method for brightness and contrast enhancement includes computing a luminance histogram of a digital image, computing first distances from the luminance histogram to a plurality of predetermined luminance histograms, estimating first control point values for a global tone mapping curve from predetermined control point values corresponding to a subset of the predetermined luminance histograms selected based on the computed first distances, and interpolating the estimated control point values to determine the global tone mapping curve. The method may also include dividing the digital image into a plurality of image blocks, and enhancing each pixel in the digital image by computing second distances from a pixel in an image block to the centers of neighboring image blocks, and computing an enhanced pixel value based on the computed second distances, predetermined control point values corresponding to the neighboring image blocks, and the global tone mapping curve.