Video Encoding Adaptive Quantization via Signal-Dependent Parameters
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Solution Overview
Problem
Current adaptive quantization techniques for video encoding require significant signalling bits for mode decisions at small unit levels, such as 4×4 blocks, which reduces coding efficiency due to the large number of possible modes, leading to a preference for avoiding signalling at these levels.
Innovation Solution
The proposed method involves inverse-quantizing transform coefficients and deriving a quantization parameter based on them, allowing for adaptive quantization in video encoding and decoding to enhance subjective image quality and encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If adaptive quantization techniques are applied at small unit level (e.g., 4×4 block) to improve coding efficiency, then coding efficiency is improved, but the number of signalling bits increases significantly
Solution Approach 1:
The patent applies parameter changes by deriving quantization parameters dynamically based on transform coefficient characteristics rather than using fixed or pre-determined parameters. The quantization parameter is adjusted according to the actual signal content and transform domain properties, enabling adaptive quantization that reduces signalling overhead while maintaining coding efficiency. This is achieved by changing the parameter selection strategy from static to dynamic based on signal-dependent analysis.
2Adaptability or versatility
If multiple modes of operations are provided for adaptive quantization to enhance flexibility, then adaptability is improved, but the complexity of mode decision signalling increases
Solution Approach 1:
The patent extracts the essential information needed for adaptive quantization from the transform coefficients themselves, eliminating the need for separate mode decision signalling. By deriving quantization parameters directly from the transform coefficient characteristics (such as magnitude distribution, frequency content, or block properties), the system removes the complex signalling overhead associated with mode selection while retaining the flexibility and adaptability of multiple quantization strategies.
Data Source
AI summary
The encoding method includes: inverse-quantizing one or more first transform coefficients quantized; deriving a quantization parameter based on the one or more first transform coefficients inverse-quantized; and inverse-quantizing a second transform coefficient quantized, based on the derived quantization parameter.