Image Decoding Using Block-Specific Quantization Matrices
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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 due to the inability to handle zeroing out techniques.
Innovation Solution
An image encoding and decoding apparatus that utilizes different quantization matrices for different block sizes within a single image, allowing for separate quantization processing 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, then encoding efficiency is improved, but quantization control capability is lost
Solution Approach 1:
The patent applies local quality by using different quantization matrices for different block sizes (e.g., 8x8, 16x16, 32x32, 64x64). Each block size has its own customized quantization matrix that is optimized for that specific size, allowing the system to maintain quantization control capability while working with zeroing out technique. This resolves the contradiction by enabling frequency-component-based quantization control at the local block level rather than using a single universal quantization matrix.
Solution Approach 2:
The patent segments the quantization process by dividing the image into blocks of different sizes and applying separate quantization matrices to each segment. This segmentation allows the zeroing out technique to be applied to larger blocks (improving encoding efficiency) while smaller blocks maintain detailed quantization control, thus resolving the contradiction between encoding efficiency and quantization control capability.
2Device complexity
If single quantization matrix is used for all block sizes, then device complexity is reduced, but subjective image quality deteriorates
Solution Approach 1:
The patent introduces dynamics by making the quantization matrix selection adaptive based on block size. Instead of using a static single quantization matrix for all blocks, the system dynamically selects the appropriate quantization matrix according to the block size being processed. This dynamic approach improves subjective image quality while managing complexity through systematic rules for matrix selection.
Solution Approach 2:
The patent changes the parameter of quantization matrix selection based on block size. By introducing block size as a determining parameter for quantization matrix selection, the system optimizes image quality for each block size while maintaining manageable complexity through structured parameter changes rather than arbitrary selections.
3Manufacturing precision
If quantization matrices are customized for each block size, then subjective image quality is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-defining and storing multiple quantization matrices corresponding to different block sizes before the actual encoding process. This allows the decoding apparatus to directly access the appropriate quantization matrix without complex real-time calculations, improving subjective image quality while managing device complexity through pre-computed matrices.
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.