Adaptive Loop Filter Sets for Tile-Based Video Reconstruction
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently utilizing adaptive loop filters (ALF) to enhance video quality while managing bandwidth demands, particularly in high-resolution and high-complexity video content.
Innovation Solution
Implementing a method that employs multiple adaptive loop filter (ALF) parameter sets for a single picture, slice, or tile, allowing for conversion between visual media data and a bitstream, with options for ALF-Luma, filter shapes, taps, input sources, and classifier information, and incorporating non-linear clipping control to optimize filtering processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single ALF parameter set is used for the entire picture, then device complexity is reduced, but video quality and filtering precision deteriorate in high-resolution content
Solution Approach 1:
The picture is divided into multiple tiles, and each tile can independently select and apply appropriate ALF parameter sets from the plurality of available parameter sets. This segmentation allows localized optimization of filtering parameters for different regions, improving video quality without requiring a single complex parameter set for the entire picture.
Solution Approach 2:
The system dynamically selects from multiple ALF parameter sets based on picture characteristics, tile locations, and content types. The decoder can adaptively choose the most suitable parameter set for each tile or region, enabling flexible optimization of filtering performance for high-resolution content while managing computational complexity.
2Measurement precision
If multiple ALF parameter sets are employed for different regions, then filtering precision is improved, but bandwidth and processing complexity increase
Solution Approach 1:
Different ALF parameter sets are applied to different tiles or regions of the picture based on local content characteristics. Each region receives filtering parameters optimized for its specific features, achieving local optimization of filtering precision without requiring uniform high complexity across the entire picture.
Solution Approach 2:
The system changes filtering parameters (such as filter coefficients, tap configurations, and clipping values) based on the selected parameter set for each tile. This allows the filtering precision to be adapted to local content requirements while managing overall processing complexity through selective parameter application.
3Manufacturing precision
If multiple ALF parameter sets are used for high-resolution video, then video quality is enhanced, but bandwidth requirements increase
Solution Approach 1:
By segmenting the picture into tiles and allowing independent parameter set selection per tile, the system reduces the need to transmit a single large set of parameters for the entire high-resolution picture. Only the necessary parameter set indices and minimal side information need to be transmitted, reducing bandwidth overhead while maintaining video quality.
Solution Approach 2:
The system provides a plurality of ALF parameter sets but only applies the necessary subset for each specific tile or region. This partial action approach avoids transmitting and processing all possible parameter sets across the entire picture, thereby reducing bandwidth requirements while still achieving high video quality where needed.
Data Source
AI summary
A mechanism for processing video data is disclosed. The mechanism includes determining to employ a plurality of adaptive loop filter (ALF) parameter sets for a single picture, slice, or tile. A conversion can then be performed between a visual media data and a bitstream based on the ALF parameter sets.