Chroma Quantization Correction for Image Code Efficiency
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Solution Overview
Problem
Existing image encoding methods, such as HEVC and VVC, face inefficiencies in encoding artificial images due to unnecessary increases in code amount when using quantization parameters less than 4, leading to suboptimal image quality and increased data without improved compression.
Innovation Solution
Adaptive correction of quantization parameters by determining whether to perform transform processing on chroma components, using a reference value as a quantization parameter when the first quantization parameter is smaller than a reference value, and applying this reference value to all components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a quantization parameter smaller than 4 is used to increase gradations, then image quality after compression is improved, but the amount of code increases unnecessarily when transform processing is not performed
Solution Approach 1:
The patent dynamically changes the quantization parameter based on the transform processing status. When transform processing is not performed on chroma components, the quantization parameter is adjusted to a reference value (typically 4 or higher) instead of using a smaller value, thereby preventing unnecessary code increase while maintaining appropriate image quality through adaptive parameter selection
Solution Approach 2:
The patent implements a feedback mechanism where the quantization parameter for chroma components is adjusted based on whether transform processing is performed. The encoding device determines the transform status and accordingly selects the appropriate quantization parameter, creating a closed-loop control system that optimizes both image quality and code amount based on actual processing conditions
2Productivity
If transform processing is not performed on chroma components, then encoding efficiency is improved for artificial images, but image quality does not improve even if gradations are increased
Solution Approach 1:
The patent applies different quantization strategies to different color components based on local processing needs. When transform processing is skipped for chroma components (common in artificial images), the quantization parameter is adjusted specifically for chroma while luma components may use different parameters, allowing optimized encoding efficiency for artificial images without compromising overall image quality
Solution Approach 2:
The patent makes the quantization parameter dynamic rather than fixed, allowing it to change based on the transform processing status. This dynamic adjustment enables the system to adapt to different image types (natural vs. artificial) and processing modes (with or without transform), optimizing both encoding efficiency and image quality for each specific condition
Data Source
AI summary
The block to be encoded is encoded using a first quantization parameter corresponding to a coefficient of each color component in the block to be encoded when it is determined that orthogonal transform processing is to be performed on the coefficient of each color component in the block to be encoded, and the block to be encoded is encoded using a second quantization parameter obtained by correcting the first quantization parameter when it is determined that orthogonal transform processing is not to be performed on the coefficient of each color component in the block to be encoded. A predetermined determination based on the first quantization parameter and a predetermined value is performed, and the second quantization parameter is derived by correcting the first quantization parameter in accordance with a determination result of the predetermined determination.