Linear Quantization Equation for Data Compression Quality

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Solution Overview

Problem

Existing data compression methods face a dilemma in balancing bandwidth consumption and decompressed data quality, as compression can result in data loss and reduced quality, particularly when using linear quantization and dequantization techniques.

Innovation Solution

A method employing a quantization equation Yq(i,j) = [(Y(i,j) + offset) << n] / qs and a dequantization equation Ydq(i,j) = Yq(i,j) >> n, where Yq(i,j) represents coefficients in compressed data, Y(i,j) are input data coefficients, offset and qs are real numbers, and n is an integer, to compress and decompress data effectively.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of energy

If data compression is applied to reduce bandwidth consumption, then transmission efficiency is improved, but data quality is reduced due to data loss during compression

Engineering Contradiction:

Improvebandwidth consumptionVSAvoiddata quality

Core Design Contradiction:

Loss of energyVSManufacturing precision

Solution Approach 1:

The patent applies parameter changes by modifying the quantization equation to include an offset term and scaling factor. The quantization equation Yq(i,j) = [(Y(i,j) + offset) >> n] / qs allows adjustment of compression intensity and quality retention by changing parameters such as offset values, shift amounts (n), and scaling factors (qs). This enables flexible control over the trade-off between compression ratio and data quality without fundamentally changing the compression architecture.

Inventive Principle:

Principle #35Parameter changes

2Quantity of substance

If linear quantization is used to compress data, then bandwidth consumption is reduced, but decomposition accuracy deteriorates due to irreversible data loss

Engineering Contradiction:

Improvedata volumeVSAvoiddecompression accuracy

Core Design Contradiction:

Quantity of substanceVSMeasurement precision

Solution Approach 1:

The patent implements feedback by using the same offset and scaling parameters in both quantization and dequantization processes. The dequantization equation Ydq(i,j) = (Yq(i,j) × qs) << n uses the identical parameters applied during compression, creating a consistent transformation cycle. This feedback mechanism ensures that the decompression process accurately reverses the compression transformations, minimizing information loss and improving decomposition accuracy.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The offset term in the quantization equation serves as beforehand cushioning by pre-compensating for potential data loss. By adding the offset before quantization and applying the same offset during dequantization, the system creates a protective buffer that preserves data integrity through the lossy compression process, cushioning against the inherent information loss in linear quantization.

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

Data Source

PatentUS8451892B2Method and system for linear quantization and dequantization

Publication Date: 2013.05.28 ORACLE AMERICAN INC

AI summary

A method including receiving a set of input data in a first matrix format. The method further includes compressing the set of input data to obtain a first set of compressed data in a second matrix format, where compressing the set of input data includes using a quantization equation, the quantization equation including Yq(i,j)=[(Y(i,j)+offset)<<n]/qs, where Yq(i,j) represents a coefficient in a matrix of the first set of compressed data having a coordinate (i,j), Y(i,j) represents a coefficient in a matrix of the set of input data having the coordinate (i,j), offset is an integer, << is a first bit-wise shift operator, n is an integer, qs is a real number. The method also includes sending the first set of compressed data to a first destination.