Pattern-Based Block Filtering for Diagonal Video Gradients
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Solution Overview
Problem
Existing block filtering techniques, such as bilateral filtering, suffer from limited efficiency due to the use of a fixed cross pattern that restricts the number of samples used for filtering, leading to decreased performance, particularly in handling diagonal gradients.
Innovation Solution
Introduce pattern diversity in block filtering by selecting filter patterns based on the characteristics of the reconstructed samples, such as diagonal or cross patterns, to improve the efficiency of bilateral filtering by including more neighboring samples, especially those with similar values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed cross pattern is used for bilateral filtering, then the filtering process is simple and fast, but the performance is limited especially in handling diagonal gradients
Solution Approach 1:
The filter pattern is changed from a fixed cross pattern to a dynamic selection between cross pattern and diagonal pattern based on the gradient direction of the reconstructed samples. This allows the filtering process to adapt to different local characteristics, improving performance on diagonal gradients while maintaining simplicity on other patterns.
Solution Approach 2:
The invention changes the parameter of filter pattern selection by introducing gradient-based classification. Samples are classified into different categories (horizontal, vertical, diagonal, or other gradients) and different filter patterns are applied accordingly, optimizing the filtering performance for each category.
2Manufacturing precision
If more neighboring samples are included in filtering, then the filtering performance is improved, but the computational complexity increases
Solution Approach 1:
Different filter patterns are applied to different local regions based on the gradient characteristics of the samples. Horizontal and vertical gradients use the cross pattern, while diagonal gradients use the diagonal pattern. This local adaptation improves performance without requiring a uniformly complex filtering approach across the entire image.
Data Source
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AI summary
Methods (1100, 1300) and apparatuses (600, 1200) for video coding and decoding are provided. The method of video encoding includes accessing (1110) a reconstructed block corresponding to a block in a picture of a video, determining (1120) at least one filter pattern based on a property of the block and filtering (1130) the reconstructed block according to the at least one filter pattern. The method of video decoding includes accessing (1310) a reconstructed block corresponding to a block in a picture of an encoded video, determining (1320) at least one filter pattern based on a property of the block and filtering (1330) the reconstructed block according to the at least one filter pattern. A bitstream formatted to include encoded data, a computer-readable storage medium and a computer program product are also described.