Image Encoding Quantization Matrix Reuse for Rectangular Transform Blocks
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Solution Overview
Problem
The increasing complexity of quantization matrices for rectangular orthogonal transformations in VVC leads to an unnecessary increase in code amount, which affects encoding efficiency.
Innovation Solution
Generating a second quantization matrix from a first quantization matrix of size N×N to adapt to rectangular sub-blocks of size P×Q or Q×P, where P and Q are positive integers, for quantizing transformation coefficients in image encoding and decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If quantization matrices are individually defined for all orthogonal transformation shapes in VVC, then the adaptability to different transformation shapes is improved, but the code amount of quantization matrices unnecessarily increases
Solution Approach 1:
The patent applies universality by enabling a single square quantization matrix to serve multiple rectangular transformation shapes through transposition. The same base matrix is reused for both horizontal and vertical rectangular transformations by simply transposing it, eliminating the need to store separate quantization matrices for each shape while maintaining full adaptability.
Solution Approach 2:
The patent uses copying by creating a transposed version of the base quantization matrix for rectangular transformations. Instead of defining entirely new matrices for each rectangular shape, the system copies the base matrix and applies transposition to generate the appropriate variant, significantly reducing the total number of matrices that need to be encoded and stored.
2Manufacturing precision
If quantization matrices are individually defined for all orthogonal transformation shapes, then the manufacturing precision of quantization for specific shapes is improved, but the device complexity increases
Solution Approach 1:
The patent reduces device complexity by making the quantization matrix system universal. A single base matrix handles both square and rectangular transformations through transposition, simplifying matrix management, storage, and selection logic while preserving the precision needed for each specific transformation type.
3Quantity of substance
If a single quantization matrix is used for both horizontal and vertical rectangular transformations, then the code amount is reduced, but the adaptability to different transformation directions is worsened
Solution Approach 1:
The patent applies asymmetry through matrix transposition to handle directional transformations. By transposing the base quantization matrix, the system adapts the same matrix data to work correctly for both horizontal and vertical rectangular transformations, maintaining directional adaptability while using a unified matrix representation.
Data Source
AI summary
An image encoding apparatus for encoding an image comprises a generation unit configured to generate, from a first quantization matrix having a size of N×N (N is a positive integer), a second quantization matrix having a size of P×Q (P and Q are positive integers which satisfy P<N<Q, and the size of P×Q indicates a size of P in a horizontal direction and a size of Q in a vertical direction), and a quantization unit configured to quantize transformation coefficients in a sub-block having a size corresponding to the size of P×Q using the second quantization matrix.