Adaptive Image Compression Quantization for Noise and Data Load
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Solution Overview
Problem
Existing data compression methods face issues of overload, increased data amount, granular noise, and edge busyness due to inaccurate predictors or transformations, which are exacerbated by increasing codeword numbers or codebook boundaries.
Innovation Solution
An adaptive data compression method using a quantization function and scaling ratios, where a conversion matrix converts image data to determine a specific scaling ratio, and a quantization operation is performed to generate compressed data, minimizing data amount and reducing granular noise and edge busyness without overloading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of codewords is increased to solve overload, then the overload problem is reduced, but the amount of data increases
Solution Approach 1:
The patent applies dynamics by making the codebook boundaries adaptive rather than fixed. The codebook boundaries are dynamically adjusted based on the statistical characteristics of the input data, allowing the system to optimize for both overload prevention and data compression efficiency. This dynamic adaptation resolves the contradiction by enabling the codebook to resize its boundaries according to actual data distribution, preventing overload without requiring an increased number of codewords.
2Reliability
If the boundaries of the codebook are extended to prevent data from exceeding boundaries, then overload is prevented, but the sampling rate of codewords decreases resulting in more severe granular noise and edge busyness
Solution Approach 1:
The patent applies parameter changes by adjusting the codebook boundaries based on the statistical parameters of the input data. Specifically, the codebook boundaries are set according to the standard deviation and mean of the data, allowing the system to adapt the quantization range to match the actual data distribution. This resolves the contradiction by preventing overload while maintaining appropriate sampling density, thereby avoiding excessive granular noise and edge busyness.
3Manufacturing precision
If non-uniform quantization is used to reduce compression distortion, then the quantization error is reduced, but the device complexity increases
Solution Approach 1:
The patent applies parameter changes by using non-uniform quantization steps that are adapted to the statistical characteristics of the data. The quantization step size is dynamically determined based on the standard deviation of the input data, allowing finer resolution for important data ranges and coarser resolution for less critical ranges. This achieves reduced compression distortion while keeping the implementation relatively simple by using statistically-based parameter adaptation rather than complex algorithms.
Data Source
AI summary
The present invention provides a compression method, wherein the compression method includes the steps of: setting a quantization function; setting a plurality of scaling ratios; receiving image data; for a block of a frame of the image data, using a conversion matrix to convert data of the block to generate a plurality of converted data; determining a specific scaling ratio from the plurality of scaling ratios according to the plurality of converted data; and using the quantization function to perform a quantization operation on a plurality of adjusted data to generate compressed data, wherein the plurality of adjusted data are generated according to the plurality of converted data and the specific scaling ratio.