Parallel Entropy Coding Architecture for CABAC Speed Bottlenecks
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Solution Overview
Problem
Existing entropy encoding and decoding processes are bottlenecked by their serial nature, leading to inefficiencies in real-time data compression and decompression, particularly in applications like video encoding where CABAC is computationally demanding.
Innovation Solution
A parallel implementation of entropy encoders and decoders using multiple encoding/decoding elements with distributed search trees based on probability frequencies, allowing for simultaneous processing of data across multiple elements to balance workloads and improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CABAC is used for entropy coding, then compression efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent divides the single entropy encoder/decoder into multiple parallel encoding elements (e.g., first encoding element, second encoding element, etc.). Each element handles a subset of the probability models independently, allowing simultaneous processing of different data streams or portions of the same stream, thereby reducing the computational burden on each individual element while maintaining overall compression efficiency.
2Device complexity
If serial entropy encoding/decoding is used, then device complexity is reduced, but processing speed decreases
Solution Approach 1:
The patent implements parallel processing by segmenting the entropy coding function into multiple independent encoding elements that operate simultaneously. Each element processes different portions of the input data or different probability models in parallel, fundamentally changing the serial execution model to a parallel architecture, which directly addresses the speed bottleneck while distributing the computational workload.
Solution Approach 2:
The patent transitions from a single-dimensional serial processing approach to a multi-dimensional parallel processing architecture. By introducing temporal parallelism (multiple elements operating at the same time) and functional parallelism (different elements handling different probability models or data streams), the system adds a new dimension to the processing architecture, thereby achieving higher throughput without proportionally increasing device complexity.
Data Source
AI summary
An entropy encoder block for use in a context adaptive encoder and an entropy decoder block for use in a context adaptive decoder are provided.