CABAC Quantization Rate-Distortion Optimization
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Solution Overview
Problem
Existing digital video coding technologies face challenges in achieving a balance between coding rate and distortion during the quantization process, particularly when using context-based adaptive binary arithmetic coding (CABAC), as they require complex processing and may not optimize bit allocation effectively.
Innovation Solution
The proposed solution involves generating multiple sets of quantized coefficients based on the assumption that a particular coefficient is the last non-zero coefficient, estimating costs associated with CABAC for each set, and selecting the set with the lowest cost to achieve a desirable balance between coding rate and distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional quantization techniques are used without CABAC optimization, then the encoding process is simpler, but the balance between coding rate and distortion is not optimized
Solution Approach 1:
The patent applies preliminary action by estimating CABAC costs during the quantization process itself, rather than performing full CABAC encoding later. The encoder generates multiple candidate quantized coefficient sets, estimates their CABAC coding costs using simplified models, and selects the optimal set before actual CABAC encoding occurs. This preliminary cost estimation allows the system to optimize rate-distortion performance without the computational burden of performing complete CABAC encoding for each candidate.
2Manufacturing precision
If multiple sets of quantized coefficients are generated and evaluated, then bit allocation optimization improves, but computational complexity increases
Solution Approach 1:
The patent applies local quality by focusing computational effort on specific critical parameters rather than performing exhaustive evaluation. Instead of fully encoding all candidate quantized coefficient sets with complete CABAC, the system estimates coding costs using simplified models that focus on the most significant factors affecting bit allocation. This selective, localized approach to cost estimation provides sufficient optimization without the prohibitive computational complexity of exhaustive evaluation.
Solution Approach 2:
The patent uses inexpensive, simplified cost estimation models that can be rapidly computed and discarded. These simplified CABAC cost models are not meant to be perfectly accurate but provide sufficient guidance for selecting optimal quantized coefficient sets. The computational resources are spent on these temporary, disposable estimates rather than on multiple full CABAC encoding operations, achieving optimization with reduced complexity.
3Productivity
If CABAC is applied to transform coefficients, then coding efficiency improves, but the quantization process becomes more complex
Solution Approach 1:
The patent merges the quantization process with CABAC cost estimation into a unified rate-distortion optimization framework. Instead of treating quantization and CABAC encoding as separate sequential steps, the system integrates simplified CABAC cost modeling directly into the quantization decision-making process. This merging allows the quantization parameters to be selected with awareness of their impact on subsequent CABAC coding efficiency, achieving better overall performance without the complexity of fully implementing CABAC within the quantization loop.
Data Source
AI summary
This disclosure describes techniques for quantizing coefficients of a video block for a video coding process that supports context-based adaptive binary arithmetic coding (CABAC). A method may comprise generating a plurality of sets of quantized coefficients for the video block, wherein each of the sets of quantized coefficients is defined based on an assumption that a particular one of the coefficients of the video block corresponds to a last non-zero coefficient of the video block, estimating a plurality of costs associated with coding the video block via the CABAC, wherein the estimated costs are based respectively on different ones of the sets of quantized coefficients, and selecting a given one of the sets of quantized coefficients associated with a lowest cost.