Distortion-Aware Rounding Offsets for Video Quantization
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Solution Overview
Problem
Existing video compression methods struggle to achieve optimal tradeoffs between bitrate and quality due to the use of fixed rounding offsets in scalar quantization, leading to inefficient compression and increased computational complexity in advanced quantization techniques.
Innovation Solution
Implementing distortion-aware rounding offsets based on estimated distortion levels, using polynomial functions to associate distortion levels with rounding offsets, allowing for a wider range of offsets to improve the tradeoff between bitrate and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed rounding offsets are used in scalar quantization, then the quantization process is simple and fast, but the tradeoff between bitrate and quality is suboptimal
Solution Approach 1:
The patent applies dynamics by transitioning from fixed rounding offsets to distortion-aware dynamic rounding offsets. The rounding offset is no longer static but adapts based on the distortion level of transform coefficients, allowing the quantization process to dynamically adjust to different signal characteristics and achieve optimal bitrate-quality tradeoff while maintaining computational efficiency
Solution Approach 2:
The patent changes the parameter of rounding offset from a fixed value to a variable parameter that depends on distortion level. By introducing distortion level as a controlling parameter, the system can adjust rounding offsets to optimize the balance between compression efficiency and quality preservation, resolving the contradiction between simple quantization and optimal tradeoff
2Productivity
If advanced quantization techniques are used to improve quality-bitrate tradeoff, then compression efficiency improves, but computational complexity increases
Solution Approach 1:
The patent changes the parameter of rounding offset from fixed to distortion-aware variable, enabling advanced quantization techniques to achieve better compression efficiency without proportionally increasing computational complexity. The distortion level serves as a guiding parameter that simplifies the search for optimal quantization settings
Solution Approach 2:
The patent implements feedback by using distortion level information to adjust rounding offsets. The distortion level is estimated from transform coefficients and fed back to guide the quantization process, creating a closed-loop system that optimizes compression efficiency while keeping computational complexity manageable through feedback-driven adaptation
Data Source
AI summary
To achieve better tradeoffs between bitrate and quality in video encoding, an improved scalar quantizer can use distortion-aware rounding offsets based on estimated distortion levels from one or more distortion contributions. A polynomial function can be used to associate distortion level to rounding offset to provide a larger range of rounding offsets. Potentially different functions can be used to define relations in segments of a range of the distortion level. Potentially different functions can be used for different scenarios (e.g., color channels, different ranges of the distortion level). In some embodiments, a group of integer errors can be used to produce more candidate rounding offsets based on the initial rounding offset. The group of candidate rounding offsets can be used to determine a group of candidate integer levels of quantized coefficient and add more flexibility to adjust the quantization error and achieve better tradeoffs between bitrate and quality.