Image Identity Scale Calculation Using Hierarchical Quantization

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Solution Overview

Problem

Existing image identity scale calculation systems face challenges in adjusting the balance between identification capability and robustness, as they are limited by a fixed quantization method that affects their ability to determine image identity under various alteration processes.

Innovation Solution

An image identity scale calculation system that uses a hierarchical quantization method, allowing for the selection of a quantization index set based on additional information, to adjust the balance between identification capability and robustness by comparing hierarchical quantization index codes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a fixed quantization method is used to calculate identity scale, then the calculation process is simple, but the system cannot adjust the balance between identification capability and robustness

Engineering Contradiction:

Improveability to adjust balance between identification capability and robustnessVSAvoidcomplexity of quantization method

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent applies dynamics by making the quantization method adjustable rather than fixed. The system can dynamically select between different quantization methods (e.g., luminance-based, color-based, edge-based) depending on the image characteristics and required balance between identification capability and robustness. This allows the system to adapt to different scenarios without being constrained by a single fixed approach.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent changes the parameter of quantization method selection based on image properties and desired performance characteristics. By varying which quantization method is applied (luminance, color, edge, or gradient), the system can optimize the balance between identification capability and robustness for different image types and alteration scenarios.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If multiple quantization methods are provided to adjust balance between identification capability and robustness, then the adaptability improves, but the system complexity increases

Engineering Contradiction:

Improveflexibility in selecting quantization index setVSAvoidcomplexity of managing multiple quantization methods

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent applies preliminary action by pre-calculating and storing multiple quantization index sets corresponding to different quantization methods before actual image comparison. This allows the system to quickly switch between different quantization approaches without performing complex real-time calculations, reducing the operational complexity while maintaining adaptability.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent creates a universal quantization index set structure that can serve multiple purposes. The same basic framework supports different quantization methods (luminance, color, edge, gradient), allowing a single system to handle various image characteristics and requirements without needing separate dedicated systems for each method.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Reliability

If quantization indexes are calculated for each local region, then robustness against local processing alterations improves, but the calculation time increases

Engineering Contradiction:

Improverobustness against local processing alterationsVSAvoidtime for calculating quantization indexes

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent extracts only the essential quantization index information from each local region that is necessary for identity comparison. By focusing on key features (luminance, color, edge, or gradient characteristics) rather than processing all image data, the system maintains robustness against local alterations while reducing calculation time through selective feature extraction.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent applies partial action by calculating quantization indexes for a selected subset of local regions or using a reduced set of quantization methods based on image characteristics. This allows the system to achieve sufficient robustness without the excessive computational burden of analyzing every possible feature in every local region.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS8897566B2Image identity scale calculation stystem

Publication Date: 2014.11.25 NEC CORP

AI summary

This image identity scale calculation system can calculate an identity scale representing a degree of identity of two images in consideration of identification capability and robustness. An image feature comparison unit is supplied with hierarchical quantization index codes, which are encodings allowing unique specification of quantization indexes of a plurality of hierarchies calculated by hierarchical quantization for each quantization target region of the two images, and selects a quantization index set used for comparison as a comparing quantization index set based on additionally supplied information in accordance with a previously defined hierarchical quantization method. Then, the image feature comparison unit compares the hierarchical quantization index codes of the two images by using the comparing quantization index set, and calculates an identity scale of the two images.