In-loop Filtering Virtual Boundaries Subpicture Coding
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Solution Overview
Problem
The increasing demand for high-resolution, high-quality images and videos, such as 4K or 8K, poses challenges in efficient compression, transmission, storage, and reproduction due to the high amount of information required, leading to increased costs and complexity.
Innovation Solution
The proposed method enhances image/video coding efficiency by performing in-loop filtering based on virtual boundaries, efficiently applying deblocking, sample adaptive loop (SAO), and adaptive loop filtering (ALF), and signaling information related to virtual boundaries based on subpicture signaling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If in-loop filtering is performed to improve image quality, then visual quality is improved, but processing complexity and time increase
Solution Approach 1:
The patent divides the picture into subpictures and applies filtering operations independently to each subpicture. This segmentation allows the filtering process to handle large pictures by processing smaller units, reducing the complexity of any single filtering operation while maintaining overall image quality improvement.
Solution Approach 2:
The patent introduces dynamic boundary handling where virtual boundaries are defined within subpictures to control where filtering operations are applied. The boundary strength and filtering application are dynamically adjusted based on the specific subpicture and boundary characteristics, optimizing the balance between quality improvement and processing complexity.
2Manufacturing precision
If virtual boundaries are introduced for filtering control, then filtering precision is improved, but signaling complexity increases
Solution Approach 1:
The patent combines the virtual boundary signaling with the existing subpicture signaling structure. The boundary strength information is integrated into the subpicture parameter set, merging multiple signaling functions into a unified structure. This reduces the overall signaling complexity while maintaining precise control over filtering boundaries.
Solution Approach 2:
The virtual boundary signaling mechanism is designed to serve multiple purposes: it controls filtering application, defines subpicture boundaries, and provides boundary strength information. This multi-functionality reduces the need for separate signaling elements, thereby reducing overall signaling complexity while maintaining precision.
3Manufacturing precision
If deblocking and SAO filtering are applied to reduce artifacts, then visual quality is improved, but processing time increases
Solution Approach 1:
The patent segments the filtering process by applying different filtering operations (deblocking, SAO, ALF) to different subpictures based on their specific characteristics and boundary requirements. This allows the system to process only the necessary portions with appropriate filtering, reducing overall processing time while maintaining effective artifact reduction where needed.
Solution Approach 2:
The patent applies filtering operations selectively rather than uniformly across the entire picture. By using virtual boundaries and subpicture-based control, the system applies filtering only where artifacts are present or boundaries require processing, avoiding unnecessary processing time in regions where filtering would not improve quality.
4Manufacturing precision
If adaptive loop filtering is applied across subpicture boundaries, then boundary artifacts are reduced, but signaling and processing complexity increases
Solution Approach 1:
The patent segments the ALF processing by defining virtual boundaries within subpictures that separate regions with different filtering characteristics. This allows the adaptive loop filter to operate independently in each segment, reducing the complexity of calculating and applying filters across the entire picture while still effectively reducing boundary artifacts at the virtual boundaries.
Solution Approach 2:
The patent applies different ALF parameters and filtering characteristics to different subpictures and regions based on their local properties. By tailoring the filtering parameters to each specific region rather than using a uniform approach, the system reduces boundary artifacts effectively while minimizing the overall processing complexity through localized optimization.
Data Source
AI summary
A picture may be divided into sub-pictures/slices/tiles. For example, the picture may be divided into sub-picture(s), and subpicture-related information may be used for coding. The sub-picture-related information may be generated by an encoding device and transmitted to a decoding device. According to embodiments of the present document, sub-picture-related information can be efficiently signaled.