Subpicture Image Coding for Quality and Data Volume Trade-Offs
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Solution Overview
Problem
The increasing demand for high-resolution, high-quality image/video data, particularly in immersive media formats like VR and AR, leads to higher transmission and storage costs due to increased data volume, necessitating a more efficient compression technology.
Innovation Solution
Implementing filtering-based image coding methods, including in-loop filtering and subpicture-based prediction and reconstruction, to enhance compression efficiency and improve subjective/objective visual quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution, high-quality image/video data is transmitted or stored using existing media, then image quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The image is divided into multiple subpictures, and each subpicture is processed independently with its own prediction and filtering units. This segmentation allows for more efficient compression by adapting to local characteristics of different regions, reducing overall data volume while maintaining high quality.
Solution Approach 2:
Different filtering strategies and prediction methods are applied to different subpictures based on their local characteristics. This local optimization enables high-quality reconstruction in critical regions while using more compressed representations in less critical areas, resolving the contradiction between quality and data volume.
2Manufacturing precision
If in-loop filtering is applied to increase visual quality, then image quality is improved, but signaling efficiency decreases
Solution Approach 1:
The filtering process is segmented into multiple stages with different filter types applied to different regions. By dividing the filtering into selective stages rather than applying a single comprehensive filter, the signaling overhead is reduced while maintaining visual quality through targeted filtering where needed.
Solution Approach 2:
Instead of applying full in-loop filtering to all regions, the patent applies filtering selectively to specific subpictures or regions where it provides the most benefit. This partial application reduces the signaling overhead required to describe filtering parameters while maintaining adequate visual quality.
3Productivity
If subpictures are applied to improve prediction and reconstruction performance, then compression efficiency is improved, but device complexity increases
Solution Approach 1:
The image is segmented into subpictures that can be processed independently with simpler prediction and reconstruction units. This segmentation reduces the complexity of each individual processing unit while improving overall compression efficiency through adaptive processing of different regions.
Solution Approach 2:
The patent implements dynamic selection of prediction and reconstruction methods for different subpictures based on their characteristics. This dynamic adaptation allows the system to achieve high compression efficiency without requiring all processing units to handle the most complex cases, thereby reducing overall device complexity.
Data Source
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AI summary
According to embodiments described herein, sub-pictures and/or virtual boundaries can be used for coding an image. For example, sub-pictures in the current picture can be used for predicting, reconstructing, and/or filtering the current picture. Virtual boundaries can be used for filtering reconstructed samples of the current picture. Through image coding based on the subpictures and/or virtual boundaries according to embodiments described herein, the subjective/objective quality of an image can be improved, and the consumption of hardware resources necessary for the coding can be reduced.