Image Decoding Quantization Matrices for Block-Size Quality Control
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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 orthogonal transform coefficients of high frequency components are forcefully set to 0 to reduce code amount, 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 segments the quantization process by introducing separate quantization matrices for different block sizes (e.g., 4x4, 8x8, 16x16, 32x32). This allows independent quantization control for each block size, enabling precise control over frequency component quantization while maintaining the zeroing out technique for coding efficiency.
Solution Approach 2:
The patent applies local quality by using different quantization matrices tailored to specific block sizes. Each quantization matrix is optimized for its corresponding block size, allowing frequency-dependent quantization control at the local block level rather than applying a uniform quantization approach across the entire image.
2Adaptability or versatility
If a quantization matrix of the same size as conventional orthogonal transform is used, then quantization processing can be performed, but it cannot support the new zeroing out technique and frequency-based quantization control is limited
Solution Approach 1:
The patent introduces dynamic adaptability by making the quantization matrix size variable rather than fixed. The quantization matrix size is dynamically adjusted to match the block size being processed, allowing the system to adapt to different block sizes (4x4, 8x8, 16x16, 32x32) and support the zeroing out technique effectively.
Solution Approach 2:
The patent changes the parameter of quantization matrix size to match the block size. By varying the quantization matrix size according to the block size, the system can properly support the zeroing out technique for large blocks while maintaining precise frequency-based quantization control for smaller blocks.
3Device complexity
If uniform quantization is applied across all block sizes, then processing is simplified, but frequency component control is lost and image quality degradation occurs
Solution Approach 1:
The patent segments the quantization processing by introducing separate quantization matrices for different block sizes. This segmentation allows frequency-dependent quantization control for each block size while keeping the overall processing structure organized and manageable through systematic categorization.
Solution Approach 2:
The patent applies local quality by tailoring quantization matrices to specific block sizes, ensuring that each block size receives optimized quantization control appropriate to its frequency characteristics, thereby improving image quality while maintaining processing organization.
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.