Duplicate Image Detection Using Byte Containers and RMS
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Solution Overview
Problem
Existing methods for detecting duplicate images are inefficient and lack scalability, especially when dealing with varying image formats, noise, skew, and different compression formats, which complicates the identification of duplicate images across different types of documents and photographs.
Innovation Solution
A computing device processes images by generating byte containers, calculating difference data, and computing a root mean square value from histograms to determine if images are duplicates, with optional steps of format conversion, noise removal, and skew correction, using hash comparisons and graphical representations to assess similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional duplicate detection methods are used, then simplicity of implementation is maintained, but detection accuracy deteriorates due to inability to handle noise, skew, and format variations
Solution Approach 1:
The patent segments the duplicate detection process into multiple distinct stages: pre-processing (format conversion, skew correction, noise removal), feature extraction (byte container generation, histogram computation), and comparison (root mean square calculation, score evaluation). This segmentation allows each stage to be optimized independently, improving overall detection accuracy while managing complexity through modular organization.
Solution Approach 2:
The patent applies preliminary actions by performing pre-processing operations (format conversion, skew correction, noise removal) before the actual duplicate detection. These preliminary steps prepare the images in advance to ensure they are in a standardized, clean state, which significantly improves detection accuracy without adding complexity during the core comparison operation.
2Reliability
If comprehensive image processing is applied to handle all variations, then detection reliability is improved, but processing time increases
Solution Approach 1:
The patent changes parameters by converting images to a standardized byte container format and computing histograms with specific parameter settings. This standardization allows for reliable comparison across different image formats and conditions while maintaining efficient processing speeds through consistent parameter application.
Solution Approach 2:
The patent creates simplified copies of images in the form of byte containers and histograms, which are computationally lightweight representations. These copies capture the essential features needed for duplicate detection without requiring processing of the full-resolution original images, thereby maintaining reliability while reducing processing time.
3Adaptability or versatility
If multiple image formats are supported, then adaptability is improved, but processing complexity increases due to format conversion requirements
Solution Approach 1:
The patent implements universality by designing a byte container format that can represent images regardless of their original format. The system includes functionality to convert multiple image formats (JPEG, PNG, BMP, GIF) into this universal byte container representation, allowing the core duplicate detection algorithm to operate uniformly on all formats without format-specific logic.
Solution Approach 2:
The patent introduces byte containers as an intermediary representation between the diverse input image formats and the duplicate detection algorithm. This intermediary format serves as a universal bridge that standardizes images from different sources, enabling the detection system to handle various formats adaptably while keeping the core comparison logic simple and unified.
Data Source
AI summary
Various features described herein may include ways of processing multiple images to determine whether any duplicates are among the multiple images. A hashing algorithm may be used to create a hash key of an image. Multiple hash keys corresponding to multiple images may be compared to determine whether those images are duplicate images. A root mean square algorithm may be used to further identify whether multiple images are duplicate images. An image variation engine, which uses intensity coding, may be used to display differences between images. For example, similar areas in images may be drawn with low intensity or high opacity, while different areas in images may be drawn with high intensity or low opacity.