Slice-Type Context Resetting for Parallel Video Entropy Decoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
State-of-the-art video-coding methods like H.264/AVC and TMuC provide higher coding efficiency but at the expense of increased complexity, which can be mitigated by supporting parallel decoding, especially with advances in multi-core processors, but entropy decoding remains a bottleneck due to serial processing within slices.
Innovation Solution
The invention introduces the concept of entropy slices, allowing parallel decoding of video frames by partitioning reconstruction slices into self-contained entropy slices, where context models are reset at the start of each slice, enabling independent decoding and reducing dependency on neighboring slices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video coding methods use macroblock prediction followed by residual coding to reduce temporal and spatial redundancy, then coding efficiency is improved, but decoder complexity increases
Solution Approach 1:
The picture is divided into multiple slices, and each slice is further divided into entropy slices. This segmentation allows the decoder to process smaller independent units in parallel, reducing the complexity of the overall decoding process while maintaining coding efficiency through the preserved spatial and temporal prediction structures.
2Adaptability or versatility
If video coding methods use variable-sized coding tree blocks to provide flexibility, then adaptability is improved, but device complexity increases
Solution Approach 1:
The variable-sized coding tree blocks are organized into fixed-structure entropy slices with predetermined maximum sizes. This segmentation approach allows the decoder to handle flexible block sizes within a standardized processing framework, maintaining adaptability while reducing complexity through consistent slice boundary handling and parallel processing capabilities.
3Reliability
If entropy decoding is performed serially within slices, then decoding accuracy is maintained, but decoding speed decreases
Solution Approach 1:
Slices are divided into multiple entropy slices that are self-contained and can be decoded independently in parallel. Each entropy slice maintains the necessary context information for accurate decoding, allowing multiple decoding operations to proceed simultaneously without compromising decoding accuracy, thereby significantly improving overall decoding speed.
Solution Approach 2:
Context models are reset at the start of each entropy slice, preparing the decoding state in advance for independent parallel processing. This preliminary initialization ensures that each entropy slice can be decoded accurately without requiring sequential processing of previous slices, enabling parallel execution while maintaining reliability.
4Speed
If context models are reset at the start of each entropy slice to enable parallel decoding, then decoding speed is improved, but loss of information may occur
Solution Approach 1:
The picture is segmented into entropy slices with carefully designed boundaries that ensure all necessary context information is contained within each slice or can be independently reconstructed. This segmentation strategy enables parallel decoding with context model resets while preventing information loss by ensuring each entropy slice is self-contained with respect to the entropy coding process.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method for decoding a video frame of a video sequence comprising: (a) receiving a slice header; (b) identifying a slice type; (c) receiving a first flag indicating a initialization method used to initialize a context; and (d) initializing the context associated with the slice type using one of a first initialization method, second initialization method and third initialization method.