Image Decoding Using Block-Size-Specific Inverse Quantization
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Solution Overview
Problem
The existing HEVC and VVC encoding methods lack effective quantization control for zeroed-out orthogonal transform coefficients, leading to suboptimal subjective image quality due to the inability to adjust quantization based on frequency components.
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 and inverse quantization of orthogonal transform coefficients, thereby enabling improved 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 based on frequency components is lost
Solution Approach 1:
The patent segments the quantization process by introducing separate quantization matrices for different block sizes (e.g., 4×4, 8×8, 16×16, 32×32). This allows frequency-component-specific quantization control for each block size, resolving the contradiction by maintaining both compression efficiency through zeroing out and adaptability through size-specific quantization matrices.
Solution Approach 2:
The patent applies local quality by assigning different quantization matrices to different block sizes within the same image. Each block size receives customized quantization parameters that match its frequency characteristics, enabling localized optimization of both compression efficiency and image quality without compromising the other.
2Device complexity
If same quantization matrix is used for all block sizes, then device complexity is reduced, but subjective image quality deteriorates
Solution Approach 1:
The patent segments the single quantization matrix into multiple size-specific quantization matrices (4×4, 8×8, 16×16, 32×32). This segmentation enables optimized quantization for each block size, significantly improving subjective image quality while managing complexity through a systematic organization of quantization parameters by block size.
3Loss of time
If quantization is performed without block size consideration, then processing time is reduced, but encoding efficiency deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-defining multiple quantization matrices corresponding to different block sizes before the actual encoding process. This allows the encoder to quickly select and apply the appropriate quantization matrix based on block size without time-consuming calculations during encoding, thus maintaining both speed and efficiency.
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.