Predicted Pixel Generation Using Genetic Programming for Image Coding

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Solution Overview

Problem

Conventional image encoding methods lack flexibility in optimizing prediction procedures and require manual generation, limiting the complexity of prediction formulas and the ability to adapt to specific input images, leading to inefficient encoding and decoding processes.

Innovation Solution

An automated system using genetic programming to generate prediction procedures by representing algorithms as tree structures, allowing for the evolution of predictors that minimize encoding costs and adapt to input images, thereby improving prediction efficiency and reducing code size.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual generation of prediction procedures is used, then the complexity of prediction formulas is limited and ease of operation is maintained, but adaptability to specific input images and encoding efficiency deteriorate

Engineering Contradiction:

Improveadaptability to input imagesVSAvoidcomplexity of prediction procedure generation

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system enables automatic generation of prediction procedures through genetic programming, where the algorithm evolves predictors autonomously without manual intervention. The genetic programming system self-adjusts prediction formulas by evaluating multiple candidates and selecting optimal ones based on encoding efficiency metrics.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The prediction procedure transitions from static manual formulas to dynamic automatically-generated formulas. The genetic programming approach allows prediction procedures to adapt and evolve based on the specific characteristics of input images, creating optimized predictors tailored to each image's properties.

Inventive Principle:

Principle #15Dynamics

2Measurement precision

If automatic generation using genetic programming is used, then adaptability to input images and prediction accuracy improve, but device complexity and computation time increase

Engineering Contradiction:

Improveprediction accuracyVSAvoidcomplexity of predictor generation system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The genetic programming system implements feedback mechanisms where prediction accuracy is continuously evaluated and used to guide the evolution of prediction procedures. The system measures the performance of generated predictors and uses this feedback to refine and optimize subsequent generations of prediction formulas.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system automatically adjusts and optimizes parameters within prediction formulas through genetic programming. By varying parameters such as pixel weights, combination coefficients, and formula structures, the system discovers optimized parameter sets that maximize prediction accuracy for specific image characteristics.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If complex prediction procedures are manually designed, then prediction accuracy may improve, but ease of operation and implementation simplicity deteriorate

Engineering Contradiction:

Improvepixel value prediction accuracyVSAvoidsimplicity of encoding/decoding process

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system replaces manual mechanical design of prediction procedures with automated computational generation. Instead of manually crafting complex prediction formulas, the genetic programming system computationally generates optimized predictors, substituting human effort with algorithmic automation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs preliminary generation and optimization of prediction procedures before actual image encoding. By pre-generating optimized predictors using genetic programming, the system prepares ready-to-use prediction formulas that simplify the subsequent encoding and decoding operations.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS9124289B2Predicted pixel value generation procedure automatic producing method, image encoding method, image decoding method, apparatus therefor, programs therefor, and storage media which store the programs

Publication Date: 2015.09.01 NIPPON TELEGRAPH & TELEPHONE CORP

AI summary

An automatic producing method for a predicted value generation procedure that predicts a value of an encoding target pixel by using a previously-decoded pixel. A parent population is generated by randomly producing predicted value generation procedures each of which is indicated by a tree structure, and a plurality of predicted value generation procedures are selected as parents from the parent population. One or more predicted value generation procedures are generated as children based on a predetermined tree structure developing method which subjects the selected predicted value generation procedures to a development where an existing predicted value generation function can be an end node of a tree. A predicted value generation procedure having a best estimation cost is selected from among the predicted value generation procedures as the parents and children, where the sum of an information content for representing the tree structure and an amount of code evaluated by a predicted pixel value obtained by the tree structure is used as an estimation cost. A final predicted value generation procedure is produced by iterating the relevant operation.