Adaptive Golomb Image Encoding for Low-Load Parameter Updates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image encoding methods using dynamic probability distribution models face inefficiencies in determining encoding parameters, leading to increased processing load and memory requirements, especially when the statistical nature of the information source changes significantly.
Innovation Solution
An image encoding apparatus and method that includes input, encoding, judging, and updating means to determine and adjust the encoding parameter based on whether the encoded symbol exceeds or falls below target ranges, optimizing the encoding parameter for efficient encoding performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dynamic probability distribution model is used for encoding, then encoding efficiency is improved, but processing load increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the encoding parameter k based on the statistical characteristics of the information source. The system monitors the frequency distribution of symbols and adapts the Golomb encoding parameter k to match the current data characteristics, thereby optimizing encoding efficiency while managing processing complexity through controlled parameter adaptation rather than full dynamic probability modeling.
2Productivity
If encoding parameter is dynamically adjusted, then compression performance is improved, but memory requirements increase
Solution Approach 1:
The patent implements parameter changes by adapting the encoding parameter k according to the observed statistical properties of the input data. By monitoring symbol frequencies and adjusting k dynamically, the system achieves improved compression performance while maintaining manageable memory requirements through efficient parameter tracking rather than storing complete probability distribution models.
3Productivity
If Golomb encoding with dynamic parameter selection is used, then encoding efficiency is improved, but determination complexity increases
Solution Approach 1:
The patent applies parameter changes by dynamically selecting the Golomb encoding parameter k based on the statistical characteristics of the information source. The system determines the appropriate k value by analyzing the frequency distribution of symbols and adjusting the parameter to optimize encoding efficiency, thereby resolving the contradiction between encoding efficiency and determination complexity through adaptive parameter selection.
Data Source
AI summary
The present invention is able to determine an encoding parameter using a simple method with little processing load or memory cost, and enables encoding of image data with excellent compression performance. To this end, a prediction error generating unit of an encoding apparatus according to the present invention calculates the difference (prediction error) between a pixel of interest and a predicted value. A prediction order conversion unit converts the prediction error to a non-negative integer, and outputs the non-negative integer as a prediction order M(e). A Golomb encoding unit performs encoding in accordance with a k parameter supplied from a k parameter updating unit. The k parameter updating unit updates the k parameter for use in the next updating based on the prediction order M(e) of the pixel of interest and the k parameter supplied to the Golomb encoding unit.