Adaptive Quantization Mode Selection for Video Transform Coefficients
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Solution Overview
Problem
Current video encoding and decoding technologies face challenges in achieving optimal compression efficiency due to limitations in quantization modes, particularly in handling different frequency regions and prediction modes, which affect coding complexity and efficiency.
Innovation Solution
The method involves selectively enabling or switching between first and second quantization schemes based on conditions such as the location of transform coefficients within a coding block, prediction modes, and component characteristics, using dependent scalar quantization for low-frequency regions and conventional scalar quantization for high-frequency regions to optimize coding efficiency and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single quantization mode is used for all transform coefficients, then device complexity is reduced, but coding efficiency deteriorates due to inability to adapt to different frequency regions
Solution Approach 1:
The transform coefficient block is divided into multiple frequency regions (e.g., low-frequency region and high-frequency region), and different quantization modes are applied to each region independently. This segmentation allows the system to adapt to different frequency characteristics without requiring a single complex quantization mode for the entire block.
Solution Approach 2:
Different quantization modes are assigned to different frequency regions based on their specific characteristics. Low-frequency regions may use one quantization mode optimized for preserving important visual information, while high-frequency regions use another mode optimized for compression. This local optimization improves overall coding efficiency without uniformly increasing complexity across the entire block.
2Productivity
If multiple quantization modes are applied to different portions of transform coefficients, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The quantization mode selection is made dynamically based on the characteristics of each frequency region rather than using a fixed mode for the entire block. This dynamic adaptation allows the system to achieve better coding efficiency by matching quantization strategies to local frequency characteristics, while the complexity increase is managed through systematic region-based decision-making.
3Measurement precision
If conventional scalar quantization is used for all regions, then device complexity is minimized, but coding efficiency deteriorates in low-frequency regions requiring more precise quantization
Solution Approach 1:
The patent applies different quantization precision levels to different frequency regions. Low-frequency regions, which contain more visually important information, receive higher precision quantization treatment, while high-frequency regions use standard scalar quantization. This local differentiation improves measurement precision where needed without uniformly increasing complexity across all regions.
Data Source
AI summary
At least a method and an apparatus are presented for efficiently encoding or decoding video. For example, a quantization mode selection condition is obtained. A first quantization mode is selected for processing a first portion of a set of transform coefficients based on the quantization mode selection condition. A second quantization mode is selected for processing a second portion of the set of transform coefficients based on the quantization mode selection condition. The video is encoded or decoded based on the processed first and second portions of the set of transform coefficients.