Subpicture Loop Filtering Across Virtual Boundaries in Image Coding
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Solution Overview
Problem
The increasing demand for high-resolution, high-quality image/video data, particularly in fields like virtual reality and augmented reality, leads to higher transmission and storage costs due to the increased amount of information, necessitating a more efficient compression technology, especially in controlling loop filtering across virtual boundaries and signaling subpicture information.
Innovation Solution
The implementation of a method and apparatus for controlling loop filtering, including deblocking, sample adaptive offset (SAO), and adaptive loop filtering (ALF), with in-loop filtering based on virtual boundaries, and independent coding of subpictures, which can be treated as pictures, to enhance image coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If loop filtering is performed across virtual boundaries to improve image quality, then subjective and objective image quality is improved, but device complexity and hardware resource consumption increase
Solution Approach 1:
The picture is divided into multiple subpictures, each independently coded and filtered. This segmentation allows the filtering process to be applied separately to each subpicture, reducing the computational complexity compared to filtering the entire picture as one unit while maintaining image quality through boundary-aware filtering operations.
Solution Approach 2:
Different filtering operations are applied to different regions of the picture based on local characteristics. Virtual boundaries are defined to separate regions with different filtering requirements, allowing the system to apply appropriate filtering strength and type to each region, thereby improving overall image quality while optimizing resource usage.
2Manufacturing precision
If high-resolution, high-quality image/video data is transmitted to maintain image quality, then image quality is preserved, but transmission cost and storage cost increase
Solution Approach 1:
The system changes parameters such as filtering strength, boundary positions, and subpicture configurations to optimize the balance between image quality and compression efficiency. By adjusting these parameters, the system can achieve acceptable image quality at lower bitrates, reducing transmission and storage costs.
3Productivity
If in-loop filtering is applied across virtual boundaries to improve compression efficiency, then overall image/video compression efficiency is improved, but device complexity increases due to additional signaling requirements
Solution Approach 1:
The system extracts and signals only the essential information about virtual boundaries and filtering parameters in the bitstream, rather than transmitting complete filtering configurations. This selective extraction reduces signaling overhead while maintaining the ability to perform effective in-loop filtering across virtual boundaries.
Data Source
AI summary
According to embodiments of the present document, the subjective/objective quality of an image can be improved through image coding on the basis of subpictures and/or virtual boundaries, and consumption of hardware resources required for coding can be reduced.