Chroma QP Mapping in Image Decoding for Adaptive Compression
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images leads to increased transmission and storage costs due to the higher amount of information required, necessitating a more efficient image compression technique.
Innovation Solution
Deriving a chroma quantization parameter using a chroma QP mapping table based on signaled data, rather than a default table, to improve coding efficiency by reflecting the image's characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a default chroma QP mapping table is used, then the decoding process is simple, but the coding efficiency is low and does not reflect image characteristics
Solution Approach 1:
The chroma QP mapping table is pre-derived and stored in the decoding apparatus before actual decoding operations. This preliminary preparation allows the system to use image characteristic information that has been pre-processed and organized into a usable table format, improving coding efficiency without adding complexity during the actual decoding process.
Solution Approach 2:
The patent uses a chroma QP mapping table that is copied or derived from luma QP mapping table information. By copying the structural framework from the well-established luma table and adapting it for chroma components using derived characteristics, the system achieves improved efficiency while maintaining procedural simplicity.
2Manufacturing precision
If high-resolution and high-quality images are transmitted, then image quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the chroma quantization parameter based on image characteristics. By changing the QP value according to the specific properties of each image (such as complexity, content type, and visual characteristics), the system optimizes the balance between image quality and data volume, reducing unnecessary transmission and storage costs while maintaining acceptable quality.
Solution Approach 2:
The system applies different quality levels to different parts of the image data. By analyzing local image characteristics and applying adaptive quantization parameters specifically for chroma components in different regions, the system maintains high quality where needed while reducing data volume in less critical areas, thereby reducing overall transmission and storage requirements.
Data Source
AI summary
An image decoding method performed by a decoding device, according to the present document, comprises the steps of: obtaining image information through a bitstream; and generating a reconstructed picture on the basis of the image information.