Image Coding Context Grouping for Variable Coefficient Blocks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional arithmetic coding methods do not achieve sufficient coding efficiency due to inefficient context management and fixed group sizes, which hinder optimal utilization of statistical information in image data.
Innovation Solution
An image coding method that segments blocks of coefficients into groups based on frequency ranges and types, allowing for context determination and binary arithmetic coding using probability information specific to each group, with dynamic group sizes to adapt to coefficient distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the block is segmented into groups with fixed sizes, then the coding process is simplified, but the coding efficiency is insufficient
Solution Approach 1:
The patent applies dynamics by making the group sizes variable rather than fixed. The segmentation unit divides the block into groups where each group contains a variable number of coefficients based on their frequency characteristics. This dynamic segmentation allows the coding system to adapt to different coefficient distributions, improving coding efficiency while maintaining manageable process complexity through systematic rules for determining group boundaries.
2Measurement precision
If the number of contexts is increased to improve coding precision, then the coding efficiency improves, but the memory requirements increase
Solution Approach 1:
The patent applies local quality by assigning different context types to different frequency ranges within the coefficient block. Instead of using a single uniform context for all coefficients, the system categorizes coefficients into low-frequency and high-frequency groups, each with appropriate context models. This localized approach to context assignment improves coding precision for different frequency characteristics while avoiding the need to create excessive numbers of contexts, thus controlling memory requirements.
Data Source
AI summary
An image coding method for coding image data on a block-by-block basis includes: segmenting a block including coefficients of frequency components, into groups corresponding to frequency ranges based on types of the coefficients, and determining a context corresponding to each of the groups; performing, for each of the coefficients, binary arithmetic coding on the coefficient according to a probability information item corresponding to the context, the context being corresponding to the group having the coefficient; and updating, for each of probability information items, the probability information item corresponding to the context, based on the coefficient included in the group corresponding to the context, wherein in the segmenting, the block is segmented into the groups with various numbers of coefficients.