Transform Coefficient Quantization Using Trellis State Contexts
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Solution Overview
Problem
Current video coding techniques face inefficiencies in quantizing transform coefficients, particularly due to dependencies on full reconstruction of coefficient levels and interleaving of bypass and context-coded bins, which complicates arithmetic coding and increases computational complexity.
Innovation Solution
The proposed solution involves a trellis coded quantization scheme that uses a significance map driven by a partial set of syntax elements, allowing for separate context models based on the current state of the quantizer, reducing dependency on full coefficient level reconstruction and improving coding efficiency by interleaving bypass and context-coded bins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full reconstruction of coefficient levels is performed, then quantization accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial action by using only a partial set of syntax elements (e.g., significance map, greater than 1 flag) instead of full coefficient level reconstruction. The state machine is driven by these partial elements to determine quantizer selection, achieving sufficient quantization accuracy without the computational burden of complete coefficient reconstruction.
Solution Approach 2:
The patent segments the coefficient coding process into separate context models based on quantizer state. Different context models are used for different states of the trellis coded quantization, allowing independent optimization of each segment and reducing overall computational complexity while maintaining accuracy.
2Productivity
If bypass and context-coded bins are interleaved, then arithmetic coding efficiency is improved, but coding process complexity increases
Solution Approach 1:
The patent implements dynamic context modeling where the context model adapts based on the current state of the trellis coded quantization. The context model is updated dynamically as the state machine progresses through different states, allowing the coding process to respond to changing conditions and improve arithmetic coding efficiency without requiring static complex structures.
Solution Approach 2:
The patent performs preliminary determination of the quantizer state using a state machine driven by partial syntax elements before proceeding with arithmetic coding. This preliminary action allows the system to prepare the appropriate context model in advance, improving coding throughput by avoiding mid-process model switching while keeping the overall structure manageable.
3Measurement precision
If separate context models are used based on quantizer state, then coding precision is improved, but model management complexity increases
Solution Approach 1:
The patent creates a universal state machine framework that manages multiple context models through a single standardized interface. The state machine serves multiple functions: driving quantizer selection, determining context model selection, and managing state transitions. This universal framework simplifies model management despite using separate context models for different quantizer states.
Solution Approach 2:
The patent introduces a state machine as an intermediary between the partial syntax elements and the multiple context models. The state machine translates the partial syntax elements into quantizer state information, which then selects the appropriate context model. This intermediary layer decouples the complexity of managing multiple models from the rest of the system, making model management more tractable.
Data Source
AI summary
An example device includes processing circuitry configured to determine a first state of a data structure, the first state representing a first quantizer applied to a previously quantized or inverse quantized value of a previous transform coefficient of residual data for a block of the video data and update the data structure to a second state according to the first state and a parity of a partial set of syntax elements representing a partial set of a plurality of coefficient levels for the previous transform coefficient. The processing circuitry is further configured to determine a second quantizer to be used to quantize or inverse quantize a current value of a current transform coefficient according to the second state of the data structure and quantize or inverse quantize the current value of the current transform coefficient using the second quantizer.