Image Decoding Using Block-Size Quantization Matrix Switching
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Solution Overview
Problem
The existing High Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC) methods lack effective quantization control for orthogonal transform coefficients, leading to suboptimal subjective image quality when zeroing out high-frequency components.
Innovation Solution
An image encoding and decoding apparatus that uses different quantization matrices for different block sizes within a single image, allowing for separate quantization and inverse quantization of orthogonal transform coefficients, thereby improving subjective image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If zeroing out technique is used to reduce code amount by setting high frequency orthogonal transform coefficients to 0, then encoding efficiency is improved, but quantization control based on frequency components is lost and subjective image quality cannot be improved
Solution Approach 1:
The patent divides the quantization process into separate stages: first applying zeroing out to select significant coefficients, then applying frequency-based quantization control to those selected coefficients. This segmentation allows both compression efficiency and quality control to be achieved independently at different processing stages.
Solution Approach 2:
The zeroing out technique is applied as a preliminary action before quantization control. By first setting high frequency coefficients to zero based on block size, the patent prepares the coefficient data in advance, then applies frequency-based quantization control to the remaining significant coefficients, achieving both compression and quality control.
2Device complexity
If a single quantization matrix is used for all block sizes, then device complexity is reduced, but adaptability to different block sizes is insufficient and subjective image quality cannot be optimized
Solution Approach 1:
The patent segments the quantization matrix application process by block size. Different quantization matrices are selected and applied according to the specific block size being processed (e.g., 4x4, 8x8, 16x16, 32x32, 64x64). This allows optimization for each block size while maintaining manageable complexity through systematic organization.
Solution Approach 2:
The patent implements dynamic selection of quantization matrices based on block size. Instead of using a fixed single matrix, the system adapts the quantization parameters dynamically according to the characteristics of each block, improving subjective image quality while organizing the complexity through structured decision logic.
Data Source
AI summary
Decoding corresponding to a first array of quantized coefficients including an N×M array corresponding to a first block and data corresponding to a second array including an N×M array corresponding to a second block. Deriving a first array of orthogonal transform coefficients from the first array of quantized coefficients by using at least a first quantization matrix of an N×M array of elements, and derives a second array of orthogonal transform coefficients from the second array of by using at least a second quantization matrix of an N×M array of elements. Performing inverse orthogonal transform on the first array of orthogonal transform coefficients to generate a P×Q array of pixels of first prediction residuals, and performs inverse orthogonal transform on the second array of orthogonal transform coefficients to generate an N×M array of pixels of second prediction residuals.