Image Decoding Device Weighted Averaging Boundary Width Control
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Solution Overview
Problem
Existing image decoding techniques using geometric partitioning mode (GPM) have limited weighted averaging patterns, resulting in suboptimal encoding performance.
Innovation Solution
An image decoding device and method that generates multiple weighting coefficients with varying widths for division boundaries, allowing for controlled weighted averaging to improve encoding efficiency by selecting the most suitable coefficients for each sample based on distance from the boundary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a limited weighted averaging pattern is used in GPM, then the device complexity is reduced, but the encoding performance deteriorates
Solution Approach 1:
The patent segments the weighting coefficients into multiple patterns (first weighting coefficient pattern and second weighting coefficient pattern) with different division boundary widths. This allows the system to handle various blurring conditions separately, improving encoding performance without requiring a single complex weighting mechanism to handle all cases.
Solution Approach 2:
The patent introduces dynamic selection of weighting coefficient patterns based on the distance from the division boundary. Samples closer to the boundary use one pattern while samples farther away use another pattern, allowing the system to adapt to local characteristics and improve overall encoding efficiency.
2Productivity
If multiple weighting coefficient patterns are introduced, then the encoding efficiency is improved, but the device complexity increases
Solution Approach 1:
The patent applies different weighting coefficient patterns to different regions based on their distance from the division boundary. This local differentiation allows each region to receive the most appropriate weighting treatment, improving encoding efficiency while managing complexity through spatial localization.
Solution Approach 2:
The patent changes the parameter of division boundary width by introducing multiple weighting coefficient patterns with different boundary widths. This parameter variation allows the system to optimize for different blurring conditions, improving encoding efficiency without requiring a completely new weighting mechanism for each condition.
Data Source
AI summary
In an image decoding device (200) according to the present invention, a circuit: decodes control information and a quantized value; obtains a decoded transform coefficient by performing inverse quantization on the decoded quantized value; obtains a decoded prediction residual by performing inverse transform on the decoded transform coefficient; generates a first predicted sample based on a decoded sample and the decoded control information; accumulates the decoded sample; generates a second predicted sample based on the accumulated decoded sample and the decoded control information; prepares a plurality of weighting coefficients by which a width of a division boundary is different for at least one of the first predicted sample or the second predicted sample, and generates a third predicted sample in which the width of the division boundary is controlled by weighted averaging; and obtains the decoded sample by adding the decoded prediction residual and the third predicted sample.