Alpha Channel Inversion for Efficient Machine Learning

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Solution Overview

Problem

Conventional machine learning models struggle with efficient image processing of images containing an alpha channel, as they often ignore or incorrectly handle the alpha channel data, leading to deteriorated image quality due to inverted characteristics of pixel and alpha value occurrence rates.

Innovation Solution

A learning apparatus that inverts alpha channel data and adjusts the number of channels to align with traditional image data formats, allowing for efficient processing by comparing inverted data with training data to update the machine learning model parameters.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If alpha channel data is directly input to the machine learning model without inversion, then the processing follows the conventional fixed channel structure, but the image quality deteriorates due to inverted characteristics of pixel and alpha value occurrence rates

Engineering Contradiction:

Improveimage qualityVSAvoiddata processing complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent applies inversion to the alpha channel data by transforming the occurrence rate distribution of alpha values to match the occurrence rate distribution of pixel values. This is achieved by inverting the cumulative distribution function of the alpha channel, converting the inverted characteristics into conventional characteristics that the machine learning model can process effectively, thereby improving image quality without excessive complexity

Inventive Principle:

Principle #13The other way round (Inversion)

Solution Approach 2:

The patent changes the parameter distribution of the alpha channel data by transforming its occurrence rate characteristics. Through statistical analysis and distribution transformation, the alpha value parameters are adjusted to align with conventional image data patterns, enabling the machine learning model to process alpha channel data with the same effectiveness as traditional RGB or grayscale data

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If the number of channels is fixed according to traditional image data formats (RGB, grayscale), then the machine learning model structure remains simple, but it cannot efficiently process images with alpha channels that have inverted characteristics

Engineering Contradiction:

Improvealpha channel processing capabilityVSAvoidmodel structure complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent makes the machine learning model universal by enabling it to handle both conventional image data (RGB, grayscale) and alpha channel data with the same architecture. Through the inversion transformation, the model processes alpha channel data in the same manner as traditional image data, achieving multi-functionality without requiring separate processing paths or complex conditional logic

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

By inverting the alpha channel data characteristics, the patent allows the machine learning model to process alpha channels using the same fixed structure designed for conventional image data. The inversion transforms the specialized alpha channel processing requirement into a form that fits the universal model structure, maintaining simplicity while enhancing adaptability

Inventive Principle:

Principle #13The other way round (Inversion)

3Measurement precision

If alpha channel data is ignored or separated from pixel data for processing, then the conventional machine learning approach can be maintained, but the overall image processing accuracy deteriorates

Engineering Contradiction:

Improveimage processing accuracyVSAvoidprocessing method complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent merges the alpha channel processing with the pixel data processing by applying the same inversion-based machine learning approach to both. Instead of treating them separately, the method unifies the processing pipeline, allowing simultaneous optimization of both pixel and alpha channel representation while maintaining a single model structure

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent applies parameter transformation to the alpha channel data, changing its occurrence rate distribution to match pixel data characteristics. This parameter change enables the alpha channel to be processed with the same machine learning parameters and structures used for pixel data, improving overall accuracy without requiring separate processing methods

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11615609B2Learning apparatus, inferring apparatus, learning method, program, and inferring method

Publication Date: 2023.03.28 AKUSERU KK

AI summary

A learning apparatus that can realize efficient machine learning is provided.A learning apparatus that learns a set value in a machine learning model based on predetermined image data for learning includes an inverting unit that inverts data of at least a part of respective channels in the image data for learning, an input unit that inputs the inverted data to the machine learning model, an output unit that can compare data obtained by inverting data output from the machine learning model with training data, and/or data output from the machine learning model with data obtained by inverting training data, and a learning process executing unit that learns the set value according to a result of the comparison.