Adaptive Quantization Matrix Selection for Image Coding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image/video coding standards use fixed quantization matrices for entire sequences, which fail to optimize subjective image quality due to varying content within sequences, leading to suboptimal compressed image quality.
Innovation Solution
Implementing adaptive quantization by acquiring parameter information from neighbor blocks to determine optimal quantization modes for each block, allowing for varying quantization matrices based on image content features, such as block mode and motion vector information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed quantization matrix is used for the entire sequence, then device complexity is reduced and ease of operation is improved, but subjective image quality deteriorates due to varying image content
Solution Approach 1:
The image sequence is divided into multiple image blocks, and each block is quantized using a separate quantization matrix selected from a set of candidate matrices. This segmentation allows different quantization characteristics to be applied to different regions, improving overall image quality while maintaining manageable complexity through standardized block processing.
Solution Approach 2:
Different quantization matrices are applied to different image blocks based on local image characteristics such as activity level, texture, or importance. This local adaptation ensures that each region receives quantization treatment optimized for its specific content, thereby improving subjective image quality without requiring a completely new quantization system.
2Manufacturing precision
If adaptive quantization with multiple quantization matrices is implemented, then subjective image quality is improved by matching image content features, but device complexity increases
Solution Approach 1:
Multiple candidate quantization matrices are pre-prepared and stored in the system before actual image processing begins. These matrices are designed in advance to cover different image characteristics (e.g., low activity, high activity, different orientations). During processing, the system only needs to select from these pre-computed matrices rather than generating them dynamically, significantly reducing runtime complexity.
Solution Approach 2:
The system changes quantization parameters by selecting different matrices from a finite set of candidate matrices rather than continuously optimizing matrix values. This discrete parameter selection approach maintains image quality adaptability while avoiding the computational complexity of continuous parameter optimization during encoding.
Data Source
AI summary
A method for realizing adaptive quantization in image encoding and dequantization in image decoding is disclosed. In the adaptive quantization method, an image to be encoded is divided into one or more blocks, and each block is transformed to obtain one or more transform coefficients, the method comprising: acquiring parameter information of neighbor blocks of a current block; determining a quantization mode for the current block according to the parameter information of the neighbor blocks; and quantizing transform coefficients of the current block in the determined quantization mode. The quantization mode includes at least one of quantization matrix, quantization parameter and quantization step.