MTS Transform Index Parsing for Efficient Image Coding
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images/videos, including immersive media, necessitates a highly efficient image/video compression technique to reduce transmission and storage costs.
Innovation Solution
An image coding method and apparatus utilizing Multiple Transform Selection (MTS) to enhance coding efficiency by parsing a transform index based on flag information and applying inverse primary transforms to residual samples, with specific regions and scanning directions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution and high-quality images/videos are transmitted or stored, then image quality and resolution are improved, but transmission cost and storage cost increase
Solution Approach 1:
The patent applies Multiple Transform Selection (MTS) to change the transform parameters adaptively based on the characteristics of the current block. By selecting from multiple available transform types (e.g., DCT, DST, ADCT) rather than using a fixed transform, the system optimizes the compression efficiency for different image content, thereby achieving better compression ratios without sacrificing image quality
Solution Approach 2:
The system dynamically adapts the transform type selection based on the statistical properties of the current block, such as the position and magnitude of significant coefficients. This dynamic adaptation allows the encoder to optimize the transform choice for each specific block content, improving overall compression efficiency while maintaining high image quality
2Productivity
If transform index is coded using traditional methods, then simplicity is maintained, but coding efficiency is insufficient
Solution Approach 1:
The patent segments the transform coefficient block into multiple regions (first region containing significant coefficients and second region with less significant coefficients). Based on this segmentation, the system selectively applies transforms only to necessary regions and uses differentiated coding strategies for different regions, improving coding efficiency while managing complexity through structured processing
Solution Approach 2:
The system applies different coding strategies to different regions of the transform coefficient block. The first region with significant coefficients receives more detailed processing and appropriate transform selection, while the second region uses simplified processing. This local differentiation optimizes coding efficiency without requiring complex processing everywhere
Data Source
AI summary
An image decoding method according to the present document comprises a step of applying an inverse primary transform to a transform coefficient so as to derive residual samples for a current block, wherein: the inverse primary transform is performed on the basis of an MTS index received from a bitstream; the MTS index is parsed on the basis of an effective coefficient not existing in a second area that excludes the left top first area of the current block; and the first area is a left top 16×16 area of the current block.