CABAC Pipeline Architecture for Shared Context Model Coding
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Solution Overview
Problem
Current video coding and decoding systems, particularly in the context of H.264/AVC, face inefficiencies in processing and memory usage due to the lack of integration and pipeline architecture in context-based adaptive binary arithmetic coding (CABAC) schemes, leading to suboptimal performance in encoding and decoding processes.
Innovation Solution
A system and method for context-based adaptive binary arithmetic encoding and decoding that integrates encoding and decoding processes in a pipeline architecture, utilizing a calculating device for index calculation, memory for context model storage, and a binary arithmetic unit for encoding and decoding, with optimized context model management and processing stages to enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CABAC encoding and decoding are processed separately without integration, then each process can be independently optimized, but processing time and system complexity increase due to duplicate initialization and model selection operations
Solution Approach 1:
The patent merges the CABAC encoding and decoding processes into a single integrated system. The binary arithmetic unit is configured to perform both encoding and decoding operations using the same context models stored in memory, eliminating the need for separate processing chains. This integration shares common components including context model storage, probability state management, and arithmetic coding/decoding logic, thereby reducing overall system complexity while improving processing efficiency through unified resource utilization.
2Measurement precision
If context models are stored individually for each bin, then encoding precision is maximized, but memory usage increases significantly
Solution Approach 1:
The patent implements universal context models that serve multiple bins and syntax elements. Instead of storing separate context models for each bin, the system uses a shared set of context models in memory that can be selectively applied to different bins based on their context model indices. This multi-functional approach maintains encoding precision by allowing specific context models to be selected for each bin while reducing memory usage through shared storage of context model data across multiple operations.
3Speed
If a pipeline architecture is not used, then implementation is simpler, but processing speed and throughput are reduced
Solution Approach 1:
The patent segments the CABAC processing into distinct pipeline stages including context model selection, binary arithmetic encoding/decoding, and bit generation/consumption phases. Each stage is implemented as a separate functional unit that processes data in a predetermined sequence, allowing parallel execution of multiple operations simultaneously. This segmentation enables high-speed processing through pipelining while managing architectural complexity by organizing functions into modular, staged components with defined data flow between stages.
Data Source
AI summary
A system is disclosed for context-based adaptive binary arithmetic encoding and decoding. The system comprises a calculating device configured to calculate an index value for one of a first bin to be encoded and a second bin to be decoded, a memory device to store context models in cluster, wherein the index value for one of the first bin and the second bin is related to one of the context models, and a binary arithmetic unit configured to serve as one of an encoder for encoding the first bin based on the one context model and a decoder for decoding the second bin based on the one context model.