In-Loop Filtering Signaling for High-Resolution Image 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, which increases costs and resource consumption.
Innovation Solution
The proposed method and apparatus enhance image coding efficiency by efficiently applying deblocking, sample adaptive offset (SAO), and adaptive loop filtering (ALF) techniques, and perform in-loop filtering based on virtual boundaries, allowing for effective signaling of filtering-related information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution, high-quality image/video (4K or 8K) is transmitted and stored using existing media and storage systems, then image quality is improved, but transmission cost and storage cost increase due to the high amount of information
Solution Approach 1:
The picture is divided into multiple subpictures, each of which can be independently coded and processed. This segmentation allows for more efficient compression by applying different coding strategies to different regions, reducing the overall amount of information needed while maintaining high image quality.
Solution Approach 2:
Different filtering processes (deblocking, SAO, ALF) are selectively applied to different regions or blocks within the picture based on local characteristics. This local quality approach ensures that high-quality processing is applied where needed while reducing processing overhead in other areas, thereby reducing total information requirements.
2Manufacturing precision
If in-loop filtering is applied to improve visual quality of reconstructed pictures, then subjective/objective visual quality is improved, but device complexity and hardware resource consumption increase
Solution Approach 1:
The picture is divided into multiple subpictures that can be independently filtered. This segmentation reduces the complexity of in-loop filtering by allowing parallel processing of smaller regions rather than processing the entire picture at once, thereby reducing hardware resource consumption while maintaining visual quality.
Solution Approach 2:
Filtering operations are selectively applied only to certain blocks or regions where they are most beneficial, rather than uniformly across the entire picture. This partial action approach reduces the overall computational load and hardware requirements while still achieving improved visual quality where it matters most.
3Manufacturing precision
If multiple filtering processes (deblocking, SAO, ALF) are applied to reconstructed blocks, then visual quality is improved, but processing time and computational complexity increase
Solution Approach 1:
Different filtering processes are selectively applied to different blocks based on their specific characteristics and requirements. Not every block receives all three filtering processes (deblocking, SAO, ALF), which reduces overall processing time while maintaining visual quality in regions where filtering is most beneficial.
Solution Approach 2:
The picture is divided into subpictures and further into blocks that can be processed independently with different filtering strategies. This segmentation enables parallel processing and optimizes the application of filtering processes, reducing total processing time while maintaining quality.
Data Source
AI summary
An image decoding method according to embodiments of the present document may comprise the steps of: acquiring, via a bitstream, image information including residual information; generating reconstruction samples on the basis of the residual information; and performing an in-loop filtering procedure for the reconstruction samples so as to generate modified reconstruction samples. The step of generating of the modified reconstruction samples may comprise a step of determining whether the in-loop filtering procedure is performed across virtual boundaries. In an example, wherein the image information includes an SPS, and on the basis of whether reference picture resampling is available, whether the SPS includes additional virtual boundary-related information may be determined.