Structure Histograms for Image Enhancement
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Solution Overview
Problem
Conventional image histograms are insensitive to the ordering of pixels in an image, making them inadequate for performing all image enhancement operations effectively, as they fail to capture differences between images with the same pixel composition but different structures.
Innovation Solution
The use of a Structure Histogram, which captures information about the relative placement of pixels by considering the values of neighboring pixels, allowing for the generation of filter parameter values that enhance images based on their structural characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional histograms are used to represent image pixel distribution, then the histogram calculation is simple and fast, but the histogram is completely insensitive to the ordering of pixels and cannot differentiate between images with the same pixel composition but different structures
Solution Approach 1:
The patent transitions from conventional 1D histograms that only count pixel value frequencies to 2D structure histograms that also encode spatial relationships. Each bin in the structure histogram represents not just a pixel value but also its positional context relative to neighboring pixels, adding a spatial dimension to the traditional histogram representation. This allows differentiation of images with identical pixel compositions but different arrangements.
Solution Approach 2:
The patent segments the image analysis into two complementary components: conventional histogram analysis for overall pixel value distribution and structure histogram analysis for spatial arrangement patterns. By dividing the enhancement task into these segments, the system can process each type of information appropriately and combine them for comprehensive image enhancement.
2Device complexity
If conventional histograms are used for image enhancement, then the enhancement process is computationally efficient, but the enhancement operations cannot account for differences in image structure and ordering
Solution Approach 1:
The structure histogram serves multiple functions simultaneously: it characterizes image structure for enhancement decisions, provides spatial context for filter application, and enables differentiation of images with identical conventional histograms. This multi-functionality allows a single data structure to support diverse enhancement operations without requiring separate specialized structures for each function.
Solution Approach 2:
The structure histogram acts as an intermediary between the raw image data and the enhancement algorithms. Rather than directly analyzing pixel arrangements or implementing complex spatial algorithms, the patent uses the structure histogram as a mediating representation that captures essential spatial information in a form that enhancement algorithms can efficiently process.
3Loss of information
If pixel ordering and structure are captured in the histogram, then image structure information is preserved for enhancement, but the histogram computation and storage requirements increase
Solution Approach 1:
The structure histogram implements local quality by creating separate bins for different spatial contexts rather than using a single global histogram. Each local region or spatial relationship type has its own bins, allowing the histogram to capture localized structural patterns without requiring excessive data volume. This localized approach efficiently represents structure information where it is most relevant.
Data Source
AI summary
Systems, methods, and computer readable media expounding “Structure Histograms” and their use are presented. Structure Histograms may be described as histograms whose individual entries (values) express a functional relationship between a given pixel (or group of pixels) and its neighboring pixels. As such, Structure Histograms capture information about the structure of an image in so far as they record information related to the relative placement of pixels within an image (e.g., are pixels of a common value closely spaced or spaced far apart). Structure Histograms may be generated from tonal pixel values (e.g., red, green and blue values), luminance pixel values (e.g., gray scale pixel values) or cross-channel pixel values (e.g., red-luminance, green-blue, or green-blue-luminance pixel values). Structure Histograms may be used in a number of image processing contexts such as, for example, automated image enhancement operations.