Image Decoding Using Subpicture Partitioning and Quantization
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images has led to a need for more efficient image compression techniques to reduce transmission and storage costs.
Innovation Solution
The method involves obtaining image information including partitioning information and prediction information for a current picture from a bitstream, deriving subpictures and slices based on the partitioning information, and decoding the current picture using these subpictures and slices, with flags indicating the presence of subpicture information and whether each subpicture includes only one slice.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data is transmitted using conventional wired/wireless broadband lines and stored using existing storage media, then high-resolution and high-quality images can be transmitted and stored, but the transmission cost and storage cost increase
Solution Approach 1:
The picture is divided into multiple slices and subpictures, allowing selective decoding and processing of different regions. This segmentation enables more efficient compression by applying different coding strategies to different parts of the image, reducing overall transmission and storage requirements while maintaining high quality.
Solution Approach 2:
The patent employs multiple quantization parameters (qpSlice, qpSubpic, qpBt, qpTb) to control compression levels in different regions and at different stages of the coding process. By dynamically adjusting these parameters, the system optimizes the balance between image quality and compression ratio, reducing transmission and storage costs.
2Productivity
If a picture is divided into multiple slices and subpictures with complex partitioning, then decoding flexibility and compression efficiency improve, but the complexity of deriving and managing partitioning information increases
Solution Approach 1:
The picture is systematically divided into slices and subpictures with clear hierarchical relationships. Each slice contains one or more subpictures, and each subpicture contains one or more CTUs. This structured segmentation provides decoding flexibility while maintaining manageable complexity through well-defined boundaries and relationships.
Solution Approach 2:
Partitioning information including slice and subpicture boundaries is derived and established before the actual decoding process begins. The quantization parameters and other coding settings are predetermined for each partition, allowing the decoder to efficiently process each region without complex runtime decisions.
3Device complexity
If quantization parameters are applied uniformly across the entire picture, then the decoding process is simpler, but compression efficiency and image quality are reduced
Solution Approach 1:
Different quantization parameters are applied to different regions of the picture based on their importance and content characteristics. Slice-level quantization parameters (qpSlice) and subpicture-level quantization parameters (qpSubpic) allow fine-grained control, enabling better compression efficiency and image quality by using coarser quantization in less important regions and finer quantization in critical areas.
Solution Approach 2:
The patent implements multiple levels of quantization parameter variation: picture-level (qpBase), slice-level (qpSlice), and subpicture-level (qpSubpic). These parameters can be adjusted independently to optimize compression for different regions, significantly improving overall compression efficiency compared to uniform quantization.
Data Source
AI summary
A method by which a decoding apparatus decodes an image, according to the present disclosure, can signal slice-associated information on the basis of a flag related to whether there is sub-picture information and a flag related to whether a sub-picture includes a single slice.