Adaptive Deblocking Filter for Weighted Prediction Video Blocks
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Solution Overview
Problem
Existing video compression systems, such as HEVC and VVC, face challenges in efficiently reducing blocking artifacts when dealing with new coding modes that use non-square or non-rectangular blocks and weighted prediction methods.
Innovation Solution
A method for deblocking an image that determines the boundary strength between blocks based on their prediction modes and weighted prediction values, allowing for adaptive filtering to reduce blocking artifacts effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional square or rectangular block partitioning is used for deblocking filtering, then the deblocking filter can be applied with standard boundary strength determination, but it cannot effectively handle non-square or non-rectangular blocks used in new coding modes
Solution Approach 1:
The deblocking filter adapts its operation dynamically based on the prediction mode of adjacent blocks. When weighted prediction modes (GBi, BCW, MH, CIIP) are detected, the filter adjusts boundary strength determination accordingly, allowing it to handle both traditional and new coding modes effectively without requiring separate filter structures
Solution Approach 2:
The invention changes the parameters used for boundary strength determination based on prediction mode. For weighted prediction modes, additional parameters such as weight values and reference picture indices are considered, while traditional modes use standard parameters. This parameter adaptation enables the filter to work effectively across different block types and prediction modes
2Manufacturing precision
If deblocking filter is applied with standard boundary strength determination, then the filtering process is simple, but blocking artifacts are not effectively reduced in blocks using weighted prediction modes
Solution Approach 1:
The boundary strength determination is customized for local conditions - specifically for blocks using weighted prediction modes. The filter examines local properties such as prediction mode flags, weight values, and reference picture indices to determine appropriate boundary strength, rather than applying a uniform approach across all blocks
Solution Approach 2:
The prediction mode analysis and boundary strength determination are performed before the actual deblocking filtering operation. This preliminary assessment of block characteristics allows the filter to be configured optimally for each boundary, improving artifact reduction effectiveness while maintaining efficient filter execution
3Measurement precision
If weighted prediction modes (GBi, BCW, MH, CIIP) are used for coding, then prediction accuracy is improved, but blocking artifacts increase at block boundaries
Solution Approach 1:
The invention converts the harmful effect of increased blocking artifacts into a benefit by using the weighted prediction parameters (weight values, reference indices) that cause the artifacts as inputs for determining boundary strength. The same parameters that create the problem are leveraged to solve it, allowing the filter to针对性地 address artifacts in weighted prediction blocks
Solution Approach 2:
The deblocking filter uses feedback from the prediction mode determination process. By analyzing the prediction mode flags and parameters from the encoding stage, the filter receives information about where and how blocking artifacts are likely to occur, and adjusts its filtering strength accordingly to reduce these artifacts while preserving prediction accuracy
Data Source
AI summary
Different implementations are described, particularly implementations for video encoding and decoding are presented including a method for deblocking an image. According to an implementation, in a method for deblocking an image, at least one boundary is determined between a first block of samples and a second block of samples; a boundary strength is determined according to at least one of a prediction mode of the first block and a prediction mode of the second block; and samples of the first and second blocks neighboring the at least one boundary are filtered according to the boundary strength. Advantageously, in case the prediction mode of the first block is a weighted prediction mode, the boundary strength further depends on the relative weight of samples used in predicting the first block of samples according to the weighted prediction mode of the first block and reciprocally for the second block.